modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
Ayham/ernie_gpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 13 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-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. -->
# bert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8056
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 3.7125 |
### Framework versions
- Transformers 4.29.2
- Pytorch 1.8.1+cu101
- Datasets 2.12.0
- Tokenizers 0.13.1
|
Ayham/robertagpt2_cnn | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
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} | 4 | null | ---
license: mit
---
### Anders Zorn on Stable Diffusion
This is the `<anders-zorn>` 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`:
|
Ayham/robertagpt2_xsum2 | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 6 | null | ---
language:
- pt
thumbnail: "Portugues BERT for the Legal Domain"
tags:
- bert
- pytorch
- tsdae
datasets:
- rufimelo/PortugueseLegalSentences-v1
license: "mit"
widget:
- text: "O advogado apresentou [MASK] ao juíz."
---
# Legal_BERTimbau
## Introduction
Legal_BERTimbau Large is a fine-tuned BERT model based on [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) Large.
"BERTimbau Base is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recognition, Sentence Textual Similarity and Recognizing Textual Entailment. It is available in two sizes: Base and Large.
For further information or requests, please go to [BERTimbau repository](https://github.com/neuralmind-ai/portuguese-bert/)."
The performance of Language Models can change drastically when there is a domain shift between training and test data. In order create a Portuguese Language Model adapted to a Legal domain, the original BERTimbau model was submitted to a fine-tuning stage where it was performed 1 "PreTraining" epoch over 50000 cleaned documents (lr: 2e-5, using TSDAE technique)
## Available models
| Model | Arch. | #Layers | #Params |
| ---------------------------------------- | ---------- | ------- | ------- |
|`rufimelo/Legal-BERTimbau-base` |BERT-Base |12 |110M|
| `rufimelo/Legal-BERTimbau-large` | BERT-Large | 24 | 335M |
## Usage
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("rufimelo/Legal-BERTimbau-large-TSDAE")
model = AutoModelForMaskedLM.from_pretrained("rufimelo/Legal-BERTimbau-large-TSDAE")
```
### Masked language modeling prediction example
```python
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("rufimelo/Legal-BERTimbau-large-TSDAE")
model = AutoModelForMaskedLM.from_pretrained("rufimelo/Legal-BERTimbau-large-TSDAE")
pipe = pipeline('fill-mask', model=model, tokenizer=tokenizer)
pipe('O advogado apresentou [MASK] para o juíz')
# [{'score': 0.5034703612327576,
#'token': 8190,
#'token_str': 'recurso',
#'sequence': 'O advogado apresentou recurso para o juíz'},
#{'score': 0.07347951829433441,
#'token': 21973,
#'token_str': 'petição',
#'sequence': 'O advogado apresentou petição para o juíz'},
#{'score': 0.05165359005331993,
#'token': 4299,
#'token_str': 'resposta',
#'sequence': 'O advogado apresentou resposta para o juíz'},
#{'score': 0.04611917585134506,
#'token': 5265,
#'token_str': 'exposição',
#'sequence': 'O advogado apresentou exposição para o juíz'},
#{'score': 0.04068068787455559,
#'token': 19737, 'token_str':
#'alegações',
#'sequence': 'O advogado apresentou alegações para o juíz'}]
```
### For BERT embeddings
```python
import torch
from transformers import AutoModel
model = AutoModel.from_pretrained('rufimelo/Legal-BERTimbau-large-TSDAE')
input_ids = tokenizer.encode('O advogado apresentou recurso para o juíz', return_tensors='pt')
with torch.no_grad():
outs = model(input_ids)
encoded = outs[0][0, 1:-1]
#tensor([[ 0.0328, -0.4292, -0.6230, ..., -0.3048, -0.5674, 0.0157],
#[-0.3569, 0.3326, 0.7013, ..., -0.7778, 0.2646, 1.1310],
#[ 0.3169, 0.4333, 0.2026, ..., 1.0517, -0.1951, 0.7050],
#...,
#[-0.3648, -0.8137, -0.4764, ..., -0.2725, -0.4879, 0.6264],
#[-0.2264, -0.1821, -0.3011, ..., -0.5428, 0.1429, 0.0509],
#[-1.4617, 0.6281, -0.0625, ..., -1.2774, -0.4491, 0.3131]])
```
## Citation
If you use this work, please cite BERTimbau's work:
```bibtex
@inproceedings{souza2020bertimbau,
author = {F{\'a}bio Souza and
Rodrigo Nogueira and
Roberto Lotufo},
title = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese},
booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)},
year = {2020}
}
```
|
Ayham/xlnet_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 13 | 2022-11-01T01:14:08Z | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: gabrielgmendonca/bert-base-portuguese-cased-finetuned-enjoei
results: []
---
# gabrielgmendonca/bert-base-portuguese-cased-finetuned-enjoei
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased)
on a teaching dataset extracted from https://www.enjoei.com.br/.
It achieves the following results on the evaluation set:
- Train Loss: 6.0784
- Validation Loss: 5.2882
- Epoch: 2
## Intended uses & limitations
This model is intended for **educational purposes**.
## 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': -985, '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: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 6.3618 | 5.7723 | 0 |
| 6.3353 | 5.4076 | 1 |
| 6.0784 | 5.2882 | 2 |
### Framework versions
- Transformers 4.23.1
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Ayta/Haha | []
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} | 0 | null | ---
tags:
- conversational
---
# Marina DialoGPT Model |
Ayumi/Jovana | []
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} | 0 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/_electricviews_/1667270688148/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/1585640841099743233/NrT5Y7dh_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">ElectricViews</div>
<div style="text-align: center; font-size: 14px;">@_electricviews_</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 ElectricViews.
| Data | ElectricViews |
| --- | --- |
| Tweets downloaded | 863 |
| Retweets | 79 |
| Short tweets | 121 |
| Tweets kept | 663 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/jeu1b88j/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 @_electricviews_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1mrestz4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1mrestz4/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/_electricviews_')
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)
|
AyushPJ/ai-club-inductions-21-nlp-XLNet | [
"pytorch",
"xlnet",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| question-answering | {
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"XLNetForQuestionAnsweringSimple"
],
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} | 9 | null | data: https://github.com/BigSalmon2/InformalToFormalDataset
Text Generation Informal Formal
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln88Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln88Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
Infill / Infilling / Masking / Phrase Masking (Works pretty decently actually, especially when you use logprobs code from above):
```
his contention [blank] by the evidence [sep] was refuted [answer]
***
few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
the joy of sport is that no two games are alike. for every exhilarating experience, however, there is an interminable one. the national pastime, unfortunately, has a penchant for the latter. what begins as a summer evening at the ballpark can quickly devolve into a game of tedium. the primary culprit is the [blank] of play. from batters readjusting their gloves to fielders spitting on their mitts, the action is [blank] unnecessary interruptions. the sport's future is [blank] if these tendencies are not addressed [sep] plodding pace [answer] riddled with [answer] bleak [answer]
***
microsoft word's [blank] pricing [blank] competition [sep] unconscionable [answer] invites [answer]
***
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
Backwards
```
Essay Intro (National Parks):
text: tourists are at ease in the national parks, ( swept up in the beauty of their natural splendor ).
***
Essay Intro (D.C. Statehood):
washington, d.c. is a city of outsize significance, ( ground zero for the nation's political life / center stage for the nation's political machinations ).
```
```
topic: the Golden State Warriors.
characterization 1: the reigning kings of the NBA.
characterization 2: possessed of a remarkable cohesion.
characterization 3: helmed by superstar Stephen Curry.
characterization 4: perched atop the league’s hierarchy.
characterization 5: boasting a litany of hall-of-famers.
***
topic: emojis.
characterization 1: shorthand for a digital generation.
characterization 2: more versatile than words.
characterization 3: the latest frontier in language.
characterization 4: a form of self-expression.
characterization 5: quintessentially millennial.
characterization 6: reflective of a tech-centric world.
***
topic:
```
```
regular: illinois went against the census' population-loss prediction by getting more residents.
VBG: defying the census' prediction of population loss, illinois experienced growth.
***
regular: microsoft word’s high pricing increases the likelihood of competition.
VBG: extortionately priced, microsoft word is inviting competition.
***
regular:
```
```
source: badminton should be more popular in the US.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) games played with racquets are popular, (2) just look at tennis and ping pong, (3) but badminton underappreciated, (4) fun, fast-paced, competitive, (5) needs to be marketed more
text: the sporting arena is dominated by games that are played with racquets. tennis and ping pong, in particular, are immensely popular. somewhat curiously, however, badminton is absent from this pantheon. exciting, fast-paced, and competitive, it is an underappreciated pastime. all that it lacks is more effective marketing.
***
source: movies in theaters should be free.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) movies provide vital life lessons, (2) many venues charge admission, (3) those without much money
text: the lessons that movies impart are far from trivial. the vast catalogue of cinematic classics is replete with inspiring sagas of friendship, bravery, and tenacity. it is regrettable, then, that admission to theaters is not free. in their current form, the doors of this most vital of institutions are closed to those who lack the means to pay.
***
source:
```
```
in the private sector, { transparency } is vital to the business’s credibility. the { disclosure of information } can be the difference between success and failure.
***
the labor market is changing, with { remote work } now the norm. this { flexible employment } allows the individual to design their own schedule.
***
the { cubicle } is the locus of countless grievances. many complain that the { enclosed workspace } restricts their freedom of movement.
***
```
```
it would be natural to assume that americans, as a people whose ancestors { immigrated to this country }, would be sympathetic to those seeking to do likewise.
question: what does “do likewise” mean in the above context?
(a) make the same journey
(b) share in the promise of the american dream
(c) start anew in the land of opportunity
(d) make landfall on the united states
***
in the private sector, { transparency } is vital to the business’s credibility. this orientation can be the difference between success and failure.
question: what does “this orientation” mean in the above context?
(a) visible business practices
(b) candor with the public
(c) open, honest communication
(d) culture of accountability
```
```
example: suppose you are a teacher. further suppose you want to tell an accurate telling of history. then suppose a parent takes offense. they do so in the name of name of their kid. this happens a lot.
text: educators' responsibility to remain true to the historical record often clashes with the parent's desire to shelter their child from uncomfortable realities.
***
example: suppose you are a student at college. now suppose you have to buy textbooks. that is going to be worth hundreds of dollars. given how much you already spend on tuition, that is going to hard cost to bear.
text: the exorbitant cost of textbooks, which often reaches hundreds of dollars, imposes a sizable financial burden on the already-strapped college student.
```
```
<Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle>
```
```
accustomed to having its name uttered ______, harvard university is weathering a rare spell of reputational tumult
(a) in reverential tones
(b) with great affection
(c) in adulatory fashion
(d) in glowing terms
```
```
clarify: international ( {working together} / cooperation ) is called for when ( {issue go beyond lots of borders} / an issue transcends borders / a given matter has transnational implications ).
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
*Note* Of all the masking techniques, this one works the best.
```
<Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle>
```
```
essence: when someone's views are keeping within reasonable.
refine: the senator's voting record is ( moderate / centrist / pragmatic / balanced / fair-minded / even-handed ).
***
essence: when things are worked through in a petty way.
refine: the propensity of the u.s. congress to settle every dispute by way of ( mudslinging / bickering / demagoguery / name-calling / finger-pointing / vilification ) is appalling.
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
```
music before bedtime [makes for being able to relax] -> is a recipe for relaxation.
```
```
[people wanting entertainment love traveling new york city] -> travelers flock to new york city in droves, drawn to its iconic entertainment scene. [cannot blame them] -> one cannot fault them [broadway so fun] -> when it is home to such thrilling fare as Broadway.
```
```
in their ( ‖ when you are rushing because you want to get there on time ‖ / haste to arrive punctually / mad dash to be timely ), morning commuters are too rushed to whip up their own meal.
***
politicians prefer to author vague plans rather than ( ‖ when you can make a plan without many unknowns ‖ / actionable policies / concrete solutions ).
```
```
Q: What is whistleblower protection?
A: Whistleblower protection is a form of legal immunity granted to employees who expose the unethical practices of their employer.
Q: Why are whistleblower protections important?
A: Absent whistleblower protections, employees would be deterred from exposing their employer’s wrongdoing for fear of retribution.
Q: Why would an employer engage in retribution?
A: An employer who has acted unethically stands to suffer severe financial and reputational damage were their transgressions to become public. To safeguard themselves from these consequences, they might seek to dissuade employees from exposing their wrongdoing.
```
```
original: the meritocratic nature of crowdfunding [MASK] into their vision's viability.
infill: the meritocratic nature of crowdfunding [gives investors idea of how successful] -> ( offers entrepreneurs a window ) into their vision's viability.
```
```
Leadership | Lecture 17: Worker Morale
What Workers Look for in Companies:
• Benefits
o Tuition reimbursement
o Paid parental leave
o 401K matching
o Profit sharing
o Pension plans
o Free meals
• Social responsibility
o Environmental stewardship
o Charitable contributions
o Diversity
• Work-life balance
o Telecommuting
o Paid holidays and vacation
o Casual dress
• Growth opportunities
• Job security
• Competitive compensation
• Recognition
o Open-door policies
o Whistleblower protection
o Employee-of-the-month awards
o Positive performance reviews
o Bonuses
``` |
AyushPJ/ai-club-inductions-21-nlp-roBERTa-base-squad-v2 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
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}
}
} | 8 | 2022-11-01T03:39:16Z | ---
language: en
thumbnail: http://www.huggingtweets.com/fienddddddd/1667274315870/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/1429983882741489668/TQAnTzje_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">Golden Boy Noah</div>
<div style="text-align: center; font-size: 14px;">@fienddddddd</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 Golden Boy Noah.
| Data | Golden Boy Noah |
| --- | --- |
| Tweets downloaded | 158 |
| Retweets | 30 |
| Short tweets | 12 |
| Tweets kept | 116 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/25q0d5x3/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 @fienddddddd's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ob718th) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ob718th/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/fienddddddd')
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)
|
AyushPJ/test-squad-trained-finetuned-squad | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 8 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/codeinecucumber-fienddddddd/1667275198553/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/1579203041764442116/RSLookYD_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/1429983882741489668/TQAnTzje_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>
<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">Gutted & Golden Boy Noah</div>
<div style="text-align: center; font-size: 14px;">@codeinecucumber-fienddddddd</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 Gutted & Golden Boy Noah.
| Data | Gutted | Golden Boy Noah |
| --- | --- | --- |
| Tweets downloaded | 1588 | 163 |
| Retweets | 234 | 30 |
| Short tweets | 298 | 12 |
| Tweets kept | 1056 | 121 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1jm5zshq/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 @codeinecucumber-fienddddddd's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1wp79eh4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1wp79eh4/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/codeinecucumber-fienddddddd')
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)
|
BJTK2/model_name | []
| null | {
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"model_type": null,
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},
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"prefix": null
},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | 2022-11-01T05:59:36Z | ---
language: "en"
tags:
- style-transfer
- text2text-generation
- seq2seq
inference: true
---
# Formality Style Transfer
## Model description
T5 Model for Formality Style Transfer. Trained on the GYAFC dataset.
## How to use
PyTorch model available.
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Isotonic/informal_to_formal")
model = AutoModelForSeq2SeqLM.from_pretrained("Isotonic/informal_to_formal")
sentence = "will you look into these two deals and let me know"
text = "Make the following sentence Formal: " + sentence + " </s>"
encoding = tokenizer.encode_plus(text,pad_to_max_length=True, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
outputs = model.generate(
input_ids=input_ids, attention_mask=attention_masks,
max_length=256,
do_sample=True,
top_k=120,
top_p=0.95,
early_stopping=True,
num_return_sequences=5
)
for output in outputs:
line = tokenizer.decode(output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
print(line)
Output: "Would you look into the two deals in question, then let me know?"
``` |
BSen/wav2vec2-base-timit-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
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}
} | 4 | 2022-11-01T07:39:34Z | ---
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: outputs
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
config: tr
split: train+validation
args: tr
metrics:
- name: Wer
type: wer
value: 0.35818608926565215
---
<!-- 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. -->
# outputs
This model was trained from scratch on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3878
- Wer: 0.3582
## 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: 64
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.7391 | 0.92 | 100 | 3.5760 | 1.0 |
| 2.927 | 1.83 | 200 | 3.0796 | 0.9999 |
| 0.9009 | 2.75 | 300 | 0.9278 | 0.8226 |
| 0.6529 | 3.67 | 400 | 0.5926 | 0.6367 |
| 0.3623 | 4.59 | 500 | 0.5372 | 0.5692 |
| 0.2888 | 5.5 | 600 | 0.4407 | 0.4838 |
| 0.285 | 6.42 | 700 | 0.4341 | 0.4694 |
| 0.0842 | 7.34 | 800 | 0.4153 | 0.4302 |
| 0.1415 | 8.26 | 900 | 0.4317 | 0.4136 |
| 0.1552 | 9.17 | 1000 | 0.4145 | 0.4013 |
| 0.1184 | 10.09 | 1100 | 0.4115 | 0.3844 |
| 0.0556 | 11.01 | 1200 | 0.4182 | 0.3862 |
| 0.0851 | 11.93 | 1300 | 0.3985 | 0.3688 |
| 0.0961 | 12.84 | 1400 | 0.4030 | 0.3665 |
| 0.0596 | 13.76 | 1500 | 0.3880 | 0.3631 |
| 0.0917 | 14.68 | 1600 | 0.3878 | 0.3582 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|
BSen/wav2vec2-large-xls-r-300m-turkish-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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}
}
} | 6 | null | ---
tags:
- generated_from_trainer
model-index:
- name: gpt2-gpt2-mc-weight0.25-epoch15-new
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-mc-weight0.25-epoch15-new
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: 4.7276
- Cls loss: 3.0579
- Lm loss: 3.9626
- Cls Accuracy: 0.6110
- Cls F1: 0.6054
- Cls Precision: 0.6054
- Cls Recall: 0.6110
- Perplexity: 52.59
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cls loss | Lm loss | Cls Accuracy | Cls F1 | Cls Precision | Cls Recall | Perplexity |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------:|:------------:|:------:|:-------------:|:----------:|:----------:|
| 4.674 | 1.0 | 3470 | 4.4372 | 1.5961 | 4.0380 | 0.5487 | 0.5279 | 0.5643 | 0.5487 | 56.71 |
| 4.3809 | 2.0 | 6940 | 4.3629 | 1.4483 | 4.0006 | 0.6023 | 0.5950 | 0.6174 | 0.6023 | 54.63 |
| 4.2522 | 3.0 | 10410 | 4.3721 | 1.5476 | 3.9849 | 0.6012 | 0.5981 | 0.6186 | 0.6012 | 53.78 |
| 4.1478 | 4.0 | 13880 | 4.3892 | 1.6429 | 3.9782 | 0.6081 | 0.6019 | 0.6128 | 0.6081 | 53.42 |
| 4.0491 | 5.0 | 17350 | 4.4182 | 1.8093 | 3.9656 | 0.6156 | 0.6091 | 0.6163 | 0.6156 | 52.75 |
| 3.9624 | 6.0 | 20820 | 4.4757 | 2.0348 | 3.9666 | 0.6121 | 0.6048 | 0.6189 | 0.6121 | 52.81 |
| 3.8954 | 7.0 | 24290 | 4.4969 | 2.1327 | 3.9634 | 0.6092 | 0.6028 | 0.6087 | 0.6092 | 52.64 |
| 3.846 | 8.0 | 27760 | 4.5632 | 2.4063 | 3.9613 | 0.6017 | 0.5972 | 0.6014 | 0.6017 | 52.52 |
| 3.8036 | 9.0 | 31230 | 4.6068 | 2.5888 | 3.9592 | 0.6052 | 0.5988 | 0.6026 | 0.6052 | 52.41 |
| 3.7724 | 10.0 | 34700 | 4.6175 | 2.6197 | 3.9621 | 0.6052 | 0.6006 | 0.6009 | 0.6052 | 52.57 |
| 3.7484 | 11.0 | 38170 | 4.6745 | 2.8470 | 3.9622 | 0.6046 | 0.5996 | 0.6034 | 0.6046 | 52.57 |
| 3.7291 | 12.0 | 41640 | 4.6854 | 2.8950 | 3.9611 | 0.6110 | 0.6056 | 0.6049 | 0.6110 | 52.52 |
| 3.7148 | 13.0 | 45110 | 4.7103 | 2.9919 | 3.9618 | 0.6063 | 0.6002 | 0.6029 | 0.6063 | 52.55 |
| 3.703 | 14.0 | 48580 | 4.7226 | 3.0417 | 3.9616 | 0.6081 | 0.6027 | 0.6021 | 0.6081 | 52.54 |
| 3.6968 | 15.0 | 52050 | 4.7276 | 3.0579 | 3.9626 | 0.6110 | 0.6054 | 0.6054 | 0.6110 | 52.59 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1 |
Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition | [
"pytorch",
"tensorboard",
"wav2vec2",
"el",
"dataset:aesdd",
"transformers",
"audio",
"audio-classification",
"speech",
"license:apache-2.0"
]
| audio-classification | {
"architectures": [
"Wav2Vec2ForSpeechClassification"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
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},
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}
}
} | 21 | null | ---
language:
- tr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-common_voice-tr-demo
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-common_voice-tr-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5682
- Wer: 0.5739
## 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: 64
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 3.69 | 100 | 3.5365 | 1.0 |
| No log | 7.4 | 200 | 2.9341 | 0.9999 |
| No log | 11.11 | 300 | 0.6994 | 0.6841 |
| No log | 14.8 | 400 | 0.5623 | 0.5792 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.11.0+cu113
- Datasets 2.6.1
- Tokenizers 0.12.1
|
BatuhanYilmaz/mlm-finetuned-imdb | []
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} | 0 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1586447193900204035/fLZqjQLG_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">MIGUEL GARGALLO ⚪️</div>
<div style="text-align: center; font-size: 14px;">@miguelgargallo</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 MIGUEL GARGALLO ⚪️.
| Data | MIGUEL GARGALLO ⚪️ |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 1884 |
| Short tweets | 231 |
| Tweets kept | 1127 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ybplkw5/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 @miguelgargallo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/5p9jhuq5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/5p9jhuq5/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='miguelgargallo/huggingtweets')
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)
|
BeIR/query-gen-msmarco-t5-large-v1 | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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},
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},
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},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 1,225 | null | ---
license: other
tags:
- stable-diffusion
- text-to-image
---
# Currently being edited. Model files are already available.
# 現在編集中です。モデルファイルは既に公開してあります。
---
# ProjectTurn8
<img src="https://i.imgur.com/WiS93wx.png" width="1000" height="">
●What is this?
We are submitting a variety of merge models that are well done.
●How to use
Put the downloaded model file into stable-diffusion-webui\models\Stable-diffusion
It is recommended to use bad_prompt_version2 of TextualInversion, but the painting style may change significantly.
●Comparison of models in the public domain.
<img src="https://i.imgur.com/j1lmHAQ.jpg" width="1000" height="">
```jsx
straw hat, (white sundress:1.2), 1 girl,loli, standing, blond hair, yellow eyes, medium breasts, outdoor, beach, cowboy shot, from outside, looking at viewer, perfect anatomy, sunlight
Negative prompt: (worst quality:1.4), (low quality:1.4),(monochrome:1.1),(bad_prompt:0.5),(swimsuit:1.2)
Steps: 36, Sampler: DPM++ SDE Karras, CFG scale: 6.5, Seed: 123, Size: 512x768, Model hash: 40ab3495, Eta: 0.67, Clip skip: 2, ENSD: 31337
```
----
## ProjectTurn8-Jupiter
●What is this?
This model is a merge of Stella and basil_mix using the extension sdweb-merge-block-weighted-gui.
Compared to Earth, the skin and clothing textures are more realistic and improved.
However, if you do not use Hires. fix, the look will be lost.
●Recommended setting
CFG Scale : 9±3
Clip skip : 2
Hires. fix : Upscale by 2
●Samples
<img src="https://i.imgur.com/zIn6hkC.png" width="400" height="">
----
## ProjectTurn8-Stella
<img src="https://i.imgur.com/qUTbReP.png" width="1000" height="">
●What is this?
This is a merged model based on anything+everything ver2.
It is mainly suited for writing 2D cute girls.
Basically, other models are created based on this model.
●Recommended setting
CFG Scale : 8±3
Clip skip : 2
----
## ProjectTurn8-Earth
<img src="https://i.imgur.com/efIyvTu.png" width="1000" height="">
●What is this?
This model was created using the extension sdweb-merge-block-weighted-gui.
It is possible to create more realistic illustrations compared to Stella.
●Recommended setting
CFG Scale : 6±1
Sampling method : DPM++ SDE Karras
----
## ProjectTurn8-Luna
<img src="https://i.imgur.com/pnVSdat.png" width="1000" height="">
●What is this?
This model is a cross between Earth and Stella.
●Recommended setting
CFG Scale : 6±1
Sampling method : DPM++ SDE Karras
|
BeIR/sparta-msmarco-distilbert-base-v1 | [
"pytorch",
"distilbert",
"feature-extraction",
"arxiv:2009.13013",
"arxiv:2104.08663",
"transformers"
]
| feature-extraction | {
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"DistilBertModel"
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}
} | 106 | 2022-11-01T11:41:26Z | ---
tags:
- conversational
---
# Melody DialoGPT Model |
BearThreat/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | {
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"DistilBertForSequenceClassification"
],
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} | 30 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/manjhunathravi/1667303320061/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/1550071946041102336/7TWTKpfv_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">Manjhunath Ravi 🚀</div>
<div style="text-align: center; font-size: 14px;">@manjhunathravi</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 Manjhunath Ravi 🚀.
| Data | Manjhunath Ravi 🚀 |
| --- | --- |
| Tweets downloaded | 3218 |
| Retweets | 2 |
| Short tweets | 287 |
| Tweets kept | 2929 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/r8x1jof9/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 @manjhunathravi's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/mmrw5vmz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/mmrw5vmz/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/manjhunathravi')
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)
|
Bee-Garbs/DialoGPT-cartman-small | []
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} | 0 | null | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
widget:
- text: This is Test
---
First Model Card |
Begimay/Task | []
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} | 0 | null | ---
license: cc-by-4.0
---
## Readability benchmark (ES): bertin-es-paragraphs-2class
This project is part of a series of models from the paper "A Benchmark for Neural Readability Assessment of Texts in Spanish".
You can find more details about the project in our [GitHub](https://github.com/lmvasque/readability-es-benchmark).
## Models
Our models were fine-tuned in multiple settings, including readability assessment in 2-class (simple/complex) and 3-class (basic/intermediate/advanced) for sentences and paragraph datasets.
You can find more details in our [paper](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share_link).
These are the available models you can use (current model page in bold):
| Model | Granularity | # classes |
|-----------------------------------------------------------------------------------------------------------|----------------|:---------:|
| **[BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-paragraphs-2class)** | **paragraphs** | **2** |
| [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-paragraphs-3class) | paragraphs | 3 |
| [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-paragraphs-2class) | paragraphs | 2 |
| [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-paragraphs-3class) | paragraphs | 3 |
| [mBERT (EN+ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-en-es-paragraphs-3class) | paragraphs | 3 |
| [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-sentences-2class) | sentences | 2 |
| [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-sentences-3class) | sentences | 3 |
| [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-sentences-2class) | sentences | 2 |
| [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-sentences-3class) | sentences | 3 |
| [mBERT (EN+ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-en-es-sentences-3class) | sentences | 3 |
For the zero-shot setting, we used the original models [BERTIN](bertin-project/bertin-roberta-base-spanish) and [mBERT](https://huggingface.co/bert-base-multilingual-uncased) with no further training.
## Results
These are our results for all the readability models in different settings. Please select your model based on the desired performance:
| Granularity | Model | F1 Score (2-class) | Precision (2-class) | Recall (2-class) | F1 Score (3-class) | Precision (3-class) | Recall (3-class) |
|-------------|---------------|:-------------------:|:---------------------:|:------------------:|:--------------------:|:---------------------:|:------------------:|
| Paragraph | Baseline (TF-IDF+LR) | 0.829 | 0.832 | 0.827 | 0.556 | 0.563 | 0.550 |
| Paragraph | BERTIN (Zero) | 0.308 | 0.222 | 0.500 | 0.227 | 0.284 | 0.338 |
| Paragraph | BERTIN (ES) | 0.924 | 0.923 | 0.925 | 0.772 | 0.776 | 0.768 |
| Paragraph | mBERT (Zero) | 0.308 | 0.222 | 0.500 | 0.253 | 0.312 | 0.368 |
| Paragraph | mBERT (EN) | - | - | - | 0.505 | 0.560 | 0.552 |
| Paragraph | mBERT (ES) | **0.933** | **0.932** | **0.936** | 0.776 | 0.777 | 0.778 |
| Paragraph | mBERT (EN+ES) | - | - | - | **0.779** | **0.783** | **0.779** |
| Sentence | Baseline (TF-IDF+LR) | 0.811 | 0.814 | 0.808 | 0.525 | 0.531 | 0.521 |
| Sentence | BERTIN (Zero) | 0.367 | 0.290 | 0.500 | 0.188 | 0.232 | 0.335 |
| Sentence | BERTIN (ES) | **0.900** | **0.900** | **0.900** | **0.699** | **0.701** | **0.698** |
| Sentence | mBERT (Zero) | 0.367 | 0.290 | 0.500 | 0.278 | 0.329 | 0.351 |
| Sentence | mBERT (EN) | - | - | - | 0.521 | 0.565 | 0.539 |
| Sentence | mBERT (ES) | 0.893 | 0.891 | 0.896 | 0.688 | 0.686 | 0.691 |
| Sentence | mBERT (EN+ES) | - | - | - | 0.679 | 0.676 | 0.682 |
## Citation
If you use our results and scripts in your research, please cite our work: "[A Benchmark for Neural Readability Assessment of Texts in Spanish](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share_link)" (to be published)
```
@inproceedings{vasquez-rodriguez-etal-2022-benchmarking,
title = "A Benchmark for Neural Readability Assessment of Texts in Spanish",
author = "V{\'a}squez-Rodr{\'\i}guez, Laura and
Cuenca-Jim{\'\e}nez, Pedro-Manuel and
Morales-Esquivel, Sergio Esteban and
Alva-Manchego, Fernando",
booktitle = "Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022",
month = dec,
year = "2022",
}
``` |
Bella4322/Sarah | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: dz_finetuning-sentiment-model-3000-samples
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. -->
# dz_finetuning-sentiment-model-3000-samples
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: 0.0553
- Accuracy: 0.99
- F1: 0.9908
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Beri/legal-qa | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
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} | 10 | 2022-11-01T12:52:16Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: twitter-prosusai-finbert-sentiment-finetuned-memes-final
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# twitter-prosusai-finbert-sentiment-finetuned-memes-final
This model is a fine-tuned version of [jayantapaul888/twitter-data-prosusai-finbert-sentiment-finetuned-memes](https://huggingface.co/jayantapaul888/twitter-data-prosusai-finbert-sentiment-finetuned-memes) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9363
- Accuracy: 0.8163
- Precision: 0.8166
- Recall: 0.8163
- F1: 0.8164
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 294 | 0.3954 | 0.8198 | 0.8215 | 0.8198 | 0.8200 |
| 0.4754 | 2.0 | 588 | 0.4318 | 0.8203 | 0.8270 | 0.8203 | 0.8204 |
| 0.4754 | 3.0 | 882 | 0.5372 | 0.8230 | 0.8230 | 0.8230 | 0.8230 |
| 0.192 | 4.0 | 1176 | 0.7378 | 0.8196 | 0.8198 | 0.8196 | 0.8195 |
| 0.192 | 5.0 | 1470 | 0.8747 | 0.8168 | 0.8176 | 0.8168 | 0.8170 |
| 0.0726 | 6.0 | 1764 | 0.9363 | 0.8163 | 0.8166 | 0.8163 | 0.8164 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|
BertChristiaens/EmojiPredictor | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: dz_finetuning_distilbert-base-uncased-finetuned-sst-2-english
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. -->
# dz_finetuning_distilbert-base-uncased-finetuned-sst-2-english
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0363
- Accuracy: 0.9933
- F1: 0.9938
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
BigSalmon/InformalToFormalLincoln14 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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} | 5 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/glxymichael-mayku/1667359514207/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/1452722152730357760/ZGwhsTpG_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/610186113500647424/jtZ7qma5_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Michael. & Zhong Liu MK Fan</div>
<div style="text-align: center; font-size: 14px;">@glxymichael-mayku</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 Michael. & Zhong Liu MK Fan.
| Data | Michael. | Zhong Liu MK Fan |
| --- | --- | --- |
| Tweets downloaded | 920 | 3206 |
| Retweets | 288 | 1004 |
| Short tweets | 31 | 80 |
| Tweets kept | 601 | 2122 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ijmau36/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 @glxymichael-mayku's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/8s85zs5e) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/8s85zs5e/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/glxymichael-mayku')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
BigSalmon/InformalToFormalLincoln22 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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"GPT2LMHeadModel"
],
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: emotions_tf_finetuned_20221101
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. -->
# emotions_tf_finetuned_20221101
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:
## 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': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.23.1
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.1
|
BigSalmon/InformalToFormalLincoln23 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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} | 5 | null | Access to model deseipel/small-LucyClarke_ is restricted and you are not in the authorized list. Visit https://huggingface.co/deseipel/small-LucyClarke_ to ask for access. |
BigSalmon/InformalToFormalLincoln24 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
} | 5 | null | ---
license: mit
tags:
- audio
- music
- generation
- tensorflow
---
# Musika Model: musika_irish_jigs
## Model provided by: rjadr
Pretrained musika_irish_jigs model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation.
Introduced in [this paper](https://arxiv.org/abs/2208.08706).
## How to use
You can generate music from this pretrained musika_irish_jigs model using the notebook available [here](https://colab.research.google.com/drive/1HJWliBXPi-Xlx3gY8cjFI5-xaZgrTD7r).
### Model description
This pretrained GAN system consists of a ResNet-style generator and discriminator. During training, stability is controlled by adapting the strength of gradient penalty regularization on-the-fly. The gradient penalty weighting term is contained in *switch.npy*. The generator is conditioned on a latent coordinate system to produce samples of arbitrary length. The latent representations produced by the generator are then passed to a decoder which converts them into waveform audio.
The generator has a context window of about 12 seconds of audio.
|
BigSalmon/MrLincoln | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8633333333333333
- name: F1
type: f1
value: 0.8646864686468646
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3097
- Accuracy: 0.8633
- F1: 0.8647
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
BigSalmon/MrLincoln10 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
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}
} | 5 | null | ---
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/bglick13/ddpm-butterflies-128/tensorboard?#scalars)
|
BigSalmon/MrLincoln125MNeo | [
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"transformers"
]
| text-generation | {
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"GPTNeoForCausalLM"
],
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}
} | 12 | null | ---
license: cc-by-4.0
---
## Readability benchmark (ES): mbert-es-paragraphs-2class
This project is part of a series of models from the paper "A Benchmark for Neural Readability Assessment of Texts in Spanish".
You can find more details about the project in our [GitHub](https://github.com/lmvasque/readability-es-benchmark).
## Models
Our models were fine-tuned in multiple settings, including readability assessment in 2-class (simple/complex) and 3-class (basic/intermediate/advanced) for sentences and paragraph datasets.
You can find more details in our [paper](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share_link).
These are the available models you can use (current model page in bold):
| Model | Granularity | # classes |
|-----------------------------------------------------------------------------------------------------------|----------------|:---------:|
| [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-paragraphs-2class) | paragraphs | 2 |
| [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-paragraphs-3class) | paragraphs | 3 |
| **[mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-paragraphs-2class)** | **paragraphs** | **2** |
| [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-paragraphs-3class) | paragraphs | 3 |
| [mBERT (EN+ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-en-es-paragraphs-3class) | paragraphs | 3 |
| [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-sentences-2class) | sentences | 2 |
| [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-sentences-3class) | sentences | 3 |
| [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-sentences-2class) | sentences | 2 |
| [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-sentences-3class) | sentences | 3 |
| [mBERT (EN+ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-en-es-sentences-3class) | sentences | 3 |
For the zero-shot setting, we used the original models [BERTIN](bertin-project/bertin-roberta-base-spanish) and [mBERT](https://huggingface.co/bert-base-multilingual-uncased) with no further training.
## Results
These are our results for all the readability models in different settings. Please select your model based on the desired performance:
| Granularity | Model | F1 Score (2-class) | Precision (2-class) | Recall (2-class) | F1 Score (3-class) | Precision (3-class) | Recall (3-class) |
|-------------|---------------|:-------------------:|:---------------------:|:------------------:|:--------------------:|:---------------------:|:------------------:|
| Paragraph | Baseline (TF-IDF+LR) | 0.829 | 0.832 | 0.827 | 0.556 | 0.563 | 0.550 |
| Paragraph | BERTIN (Zero) | 0.308 | 0.222 | 0.500 | 0.227 | 0.284 | 0.338 |
| Paragraph | BERTIN (ES) | 0.924 | 0.923 | 0.925 | 0.772 | 0.776 | 0.768 |
| Paragraph | mBERT (Zero) | 0.308 | 0.222 | 0.500 | 0.253 | 0.312 | 0.368 |
| Paragraph | mBERT (EN) | - | - | - | 0.505 | 0.560 | 0.552 |
| Paragraph | mBERT (ES) | **0.933** | **0.932** | **0.936** | 0.776 | 0.777 | 0.778 |
| Paragraph | mBERT (EN+ES) | - | - | - | **0.779** | **0.783** | **0.779** |
| Sentence | Baseline (TF-IDF+LR) | 0.811 | 0.814 | 0.808 | 0.525 | 0.531 | 0.521 |
| Sentence | BERTIN (Zero) | 0.367 | 0.290 | 0.500 | 0.188 | 0.232 | 0.335 |
| Sentence | BERTIN (ES) | **0.900** | **0.900** | **0.900** | **0.699** | **0.701** | **0.698** |
| Sentence | mBERT (Zero) | 0.367 | 0.290 | 0.500 | 0.278 | 0.329 | 0.351 |
| Sentence | mBERT (EN) | - | - | - | 0.521 | 0.565 | 0.539 |
| Sentence | mBERT (ES) | 0.893 | 0.891 | 0.896 | 0.688 | 0.686 | 0.691 |
| Sentence | mBERT (EN+ES) | - | - | - | 0.679 | 0.676 | 0.682 |
## Citation
If you use our results and scripts in your research, please cite our work: "[A Benchmark for Neural Readability Assessment of Texts in Spanish](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share_link)" (to be published)
```
@inproceedings{vasquez-rodriguez-etal-2022-benchmarking,
title = "A Benchmark for Neural Readability Assessment of Texts in Spanish",
author = "V{\'a}squez-Rodr{\'\i}guez, Laura and
Cuenca-Jim{\'\e}nez, Pedro-Manuel and
Morales-Esquivel, Sergio Esteban and
Alva-Manchego, Fernando",
booktitle = "Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022",
month = dec,
year = "2022",
}
``` |
BigSalmon/MrLincoln3 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
} | 17 | 2022-11-01T15:59:33Z | ---
license: cc-by-4.0
---
## Readability benchmark (ES): mbert-es-paragraphs-3class
This project is part of a series of models from the paper "A Benchmark for Neural Readability Assessment of Texts in Spanish".
You can find more details about the project in our [GitHub](https://github.com/lmvasque/readability-es-benchmark).
## Models
Our models were fine-tuned in multiple settings, including readability assessment in 2-class (simple/complex) and 3-class (basic/intermediate/advanced) for sentences and paragraph datasets.
You can find more details in our [paper](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share_link).
These are the available models you can use (current model page in bold):
| Model | Granularity | # classes |
|-----------------------------------------------------------------------------------------------------------|----------------|:---------:|
| [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-paragraphs-2class) | paragraphs | 2 |
| [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-paragraphs-3class) | paragraphs | 3 |
| [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-paragraphs-2class) | paragraphs | 2 |
| **[mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-paragraphs-3class)** | **paragraphs** | **3** |
| [mBERT (EN+ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-en-es-paragraphs-3class) | paragraphs | 3 |
| [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-sentences-2class) | sentences | 2 |
| [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-sentences-3class) | sentences | 3 |
| [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-sentences-2class) | sentences | 2 |
| [mBERT (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-es-sentences-3class) | sentences | 3 |
| [mBERT (EN+ES)](https://huggingface.co/lmvasque/readability-es-benchmark-mbert-en-es-sentences-3class) | sentences | 3 |
For the zero-shot setting, we used the original models [BERTIN](bertin-project/bertin-roberta-base-spanish) and [mBERT](https://huggingface.co/bert-base-multilingual-uncased) with no further training.
## Results
These are our results for all the readability models in different settings. Please select your model based on the desired performance:
| Granularity | Model | F1 Score (2-class) | Precision (2-class) | Recall (2-class) | F1 Score (3-class) | Precision (3-class) | Recall (3-class) |
|-------------|---------------|:-------------------:|:---------------------:|:------------------:|:--------------------:|:---------------------:|:------------------:|
| Paragraph | Baseline (TF-IDF+LR) | 0.829 | 0.832 | 0.827 | 0.556 | 0.563 | 0.550 |
| Paragraph | BERTIN (Zero) | 0.308 | 0.222 | 0.500 | 0.227 | 0.284 | 0.338 |
| Paragraph | BERTIN (ES) | 0.924 | 0.923 | 0.925 | 0.772 | 0.776 | 0.768 |
| Paragraph | mBERT (Zero) | 0.308 | 0.222 | 0.500 | 0.253 | 0.312 | 0.368 |
| Paragraph | mBERT (EN) | - | - | - | 0.505 | 0.560 | 0.552 |
| Paragraph | mBERT (ES) | **0.933** | **0.932** | **0.936** | 0.776 | 0.777 | 0.778 |
| Paragraph | mBERT (EN+ES) | - | - | - | **0.779** | **0.783** | **0.779** |
| Sentence | Baseline (TF-IDF+LR) | 0.811 | 0.814 | 0.808 | 0.525 | 0.531 | 0.521 |
| Sentence | BERTIN (Zero) | 0.367 | 0.290 | 0.500 | 0.188 | 0.232 | 0.335 |
| Sentence | BERTIN (ES) | **0.900** | **0.900** | **0.900** | **0.699** | **0.701** | **0.698** |
| Sentence | mBERT (Zero) | 0.367 | 0.290 | 0.500 | 0.278 | 0.329 | 0.351 |
| Sentence | mBERT (EN) | - | - | - | 0.521 | 0.565 | 0.539 |
| Sentence | mBERT (ES) | 0.893 | 0.891 | 0.896 | 0.688 | 0.686 | 0.691 |
| Sentence | mBERT (EN+ES) | - | - | - | 0.679 | 0.676 | 0.682 |
## Citation
If you use our results and scripts in your research, please cite our work: "[A Benchmark for Neural Readability Assessment of Texts in Spanish](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share_link)" (to be published)
```
@inproceedings{vasquez-rodriguez-etal-2022-benchmarking,
title = "A Benchmark for Neural Readability Assessment of Texts in Spanish",
author = "V{\'a}squez-Rodr{\'\i}guez, Laura and
Cuenca-Jim{\'\e}nez, Pedro-Manuel and
Morales-Esquivel, Sergio Esteban and
Alva-Manchego, Fernando",
booktitle = "Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022",
month = dec,
year = "2022",
}
``` |
BigSalmon/Points2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
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"GPT2LMHeadModel"
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} | 12 | null | ---
language:
- en
tags:
- stable-diffusion
- aiart
license: "creativeml-openrail-m"
---
*NOTE: usage of this model implies accpetance of stable diffusion's [CreativeML Open RAIL-M license](LICENSE)* |
BigSalmon/SimplifyText | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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} | 17 | 2022-11-01T17:00:16Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: twitter-data-xlm-roberta-base-hindi-only-memes
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# twitter-data-xlm-roberta-base-hindi-only-memes
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: 0.4006
- Accuracy: 0.9240
- Precision: 0.9255
- Recall: 0.9263
- F1: 0.9259
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.7485 | 1.0 | 511 | 0.4062 | 0.8381 | 0.8520 | 0.8422 | 0.8417 |
| 0.4253 | 2.0 | 1022 | 0.3195 | 0.8822 | 0.8880 | 0.8853 | 0.8851 |
| 0.2899 | 3.0 | 1533 | 0.2994 | 0.9031 | 0.9068 | 0.9060 | 0.9049 |
| 0.2116 | 4.0 | 2044 | 0.3526 | 0.9163 | 0.9199 | 0.9185 | 0.9187 |
| 0.1582 | 5.0 | 2555 | 0.4031 | 0.9163 | 0.9193 | 0.9186 | 0.9187 |
| 0.103 | 6.0 | 3066 | 0.4006 | 0.9240 | 0.9255 | 0.9263 | 0.9259 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Blazeolmo/Scrabunzi | []
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} | 0 | 2022-11-01T18:24:14Z | ---
license: mit
---
# ESMFold
ESMFold is a state-of-the-art end-to-end protein folding model based on an ESM-2 backbone. It does not require any lookup or MSA step, and therefore does not require any external databases to be present in order to make predictions. As a result, inference time is very significantly faster than AlphaFold2. For details on the model architecture and training, please refer to the [accompanying paper](https://www.science.org/doi/10.1126/science.ade2574).
If you're interested in using ESMFold in practice, please check out the associated [tutorial notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_folding.ipynb). |
BlightZz/DialoGPT-medium-Kurisu | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 1000
},
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}
} | 19 | 2022-11-01T18:30:11Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopterPLEv0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 11.70 +/- 11.40
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
BlindMan820/Sarcastic-News-Headlines | [
"pytorch",
"distilbert",
"text-classification",
"English",
"dataset:Kaggle Dataset",
"transformers",
"Text",
"Sequence-Classification",
"Sarcasm",
"DistilBert"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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},
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},
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}
} | 28 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# arinze/address-match-abp-v1
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('arinze/address-match-abp-v1')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('arinze/address-match-abp-v1')
model = AutoModel.from_pretrained('arinze/address-match-abp-v1')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=arinze/address-match-abp-v1)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 3125 with parameters:
```
{'batch_size': 32}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 313,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, '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})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Bloodwarrior/Chikfalay | []
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} | 0 | null | ---
tags:
- pyannote
- pyannote-audio
- pyannote-audio-model
- audio
- voice
- speech
- speaker
- speaker-segmentation
- voice-activity-detection
- overlapped-speech-detection
- resegmentation
datasets:
- ami
- dihard
- voxconverse
license: mit
inference: false
---
# 🎹 Speaker segmentation
Model from *[End-to-end speaker segmentation for overlap-aware resegmentation](http://arxiv.org/abs/2104.04045)*,
by Hervé Bredin and Antoine Laurent.
[Online demo](https://huggingface.co/spaces/pyannote/pretrained-pipelines) is available as a Hugging Face Space.
## Support
For commercial enquiries and scientific consulting, please contact [me](mailto:[email protected]).
For [technical questions](https://github.com/pyannote/pyannote-audio/discussions) and [bug reports](https://github.com/pyannote/pyannote-audio/issues), please check [pyannote.audio](https://github.com/pyannote/pyannote-audio) Github repository.
## Usage
Relies on pyannote.audio 2.0 currently in development: see [installation instructions](https://github.com/pyannote/pyannote-audio/tree/develop#installation).
### Voice activity detection
```python
from pyannote.audio.pipelines import VoiceActivityDetection
pipeline = VoiceActivityDetection(segmentation="anilbs/segmentation")
HYPER_PARAMETERS = {
# onset/offset activation thresholds
"onset": 0.5, "offset": 0.5,
# remove speech regions shorter than that many seconds.
"min_duration_on": 0.0,
# fill non-speech regions shorter than that many seconds.
"min_duration_off": 0.0
}
pipeline.instantiate(HYPER_PARAMETERS)
vad = pipeline("audio.wav")
# `vad` is a pyannote.core.Annotation instance containing speech regions
```
### Overlapped speech detection
```python
from pyannote.audio.pipelines import OverlappedSpeechDetection
pipeline = OverlappedSpeechDetection(segmentation="pyannote/segmentation")
pipeline.instantiate(HYPER_PARAMETERS)
osd = pipeline("audio.wav")
# `osd` is a pyannote.core.Annotation instance containing overlapped speech regions
```
### Resegmentation
```python
from pyannote.audio.pipelines import Resegmentation
pipeline = Resegmentation(segmentation="pyannote/segmentation",
diarization="baseline")
pipeline.instantiate(HYPER_PARAMETERS)
resegmented_baseline = pipeline({"audio": "audio.wav", "baseline": baseline})
# where `baseline` should be provided as a pyannote.core.Annotation instance
```
### Raw scores
```python
from pyannote.audio import Inference
inference = Inference("pyannote/segmentation")
segmentation = inference("audio.wav")
# `segmentation` is a pyannote.core.SlidingWindowFeature
# instance containing raw segmentation scores like the
# one pictured above (output)
```
## Reproducible research
In order to reproduce the results of the paper ["End-to-end speaker segmentation for overlap-aware resegmentation
"](https://arxiv.org/abs/2104.04045), use `pyannote/segmentation@Interspeech2021` with the following hyper-parameters:
| Voice activity detection | `onset` | `offset` | `min_duration_on` | `min_duration_off` |
| ------------------------ | ------- | -------- | ----------------- | ------------------ |
| AMI Mix-Headset | 0.684 | 0.577 | 0.181 | 0.037 |
| DIHARD3 | 0.767 | 0.377 | 0.136 | 0.067 |
| VoxConverse | 0.767 | 0.713 | 0.182 | 0.501 |
| Overlapped speech detection | `onset` | `offset` | `min_duration_on` | `min_duration_off` |
| --------------------------- | ------- | -------- | ----------------- | ------------------ |
| AMI Mix-Headset | 0.448 | 0.362 | 0.116 | 0.187 |
| DIHARD3 | 0.430 | 0.320 | 0.091 | 0.144 |
| VoxConverse | 0.587 | 0.426 | 0.337 | 0.112 |
| Resegmentation of VBx | `onset` | `offset` | `min_duration_on` | `min_duration_off` |
| --------------------- | ------- | -------- | ----------------- | ------------------ |
| AMI Mix-Headset | 0.542 | 0.527 | 0.044 | 0.705 |
| DIHARD3 | 0.592 | 0.489 | 0.163 | 0.182 |
| VoxConverse | 0.537 | 0.724 | 0.410 | 0.563 |
Expected outputs (and VBx baseline) are also provided in the `/reproducible_research` sub-directories.
## Citation
```bibtex
@inproceedings{Bredin2021,
Title = {{End-to-end speaker segmentation for overlap-aware resegmentation}},
Author = {{Bredin}, Herv{\'e} and {Laurent}, Antoine},
Booktitle = {Proc. Interspeech 2021},
Address = {Brno, Czech Republic},
Month = {August},
Year = {2021},
```
```bibtex
@inproceedings{Bredin2020,
Title = {{pyannote.audio: neural building blocks for speaker diarization}},
Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe},
Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing},
Address = {Barcelona, Spain},
Month = {May},
Year = {2020},
}
```
|
BotterHax/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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} | 8 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1099 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 1099,
"warmup_steps": 110,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Brayan/CNN_Brain_Tumor | []
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} | 0 | null | ---
license: openrail
library_name: diffusers
tags:
- TPU
- JAX
- Flax
- stable-diffusion
- text-to-image
language:
- en
---
|
Broadus20/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 1000
},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
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},
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},
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}
} | 9 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: xlm-roberta-base-eng-only-sentiment-single-finetuned-memes
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-eng-only-sentiment-single-finetuned-memes
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: 0.5629
- Accuracy: 0.8652
- Precision: 0.8794
- Recall: 0.8786
- F1: 0.8789
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 378 | 0.3506 | 0.8459 | 0.8647 | 0.8584 | 0.8605 |
| 0.4424 | 2.0 | 756 | 0.3264 | 0.8563 | 0.8818 | 0.8696 | 0.8689 |
| 0.2888 | 3.0 | 1134 | 0.3563 | 0.8578 | 0.8759 | 0.8701 | 0.8714 |
| 0.1889 | 4.0 | 1512 | 0.3939 | 0.8585 | 0.8733 | 0.8729 | 0.8730 |
| 0.1889 | 5.0 | 1890 | 0.4698 | 0.8622 | 0.8765 | 0.8761 | 0.8763 |
| 0.1136 | 6.0 | 2268 | 0.5629 | 0.8652 | 0.8794 | 0.8786 | 0.8789 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Broadus20/DialoGPT-small-joshua | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 12 | null | ---
license: openrail
library_name: diffusers
tags:
- TPU
- JAX
- Flax
- stable-diffusion
- text-to-image
language:
- en
---
|
Brokette/projetCS | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"max_length": null,
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},
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"max_length": null
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}
} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: IMDB_BERT_5E
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9533333333333334
---
<!-- 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. -->
# IMDB_BERT_5E
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2316
- Accuracy: 0.9533
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7094 | 0.03 | 50 | 0.6527 | 0.6467 |
| 0.5867 | 0.06 | 100 | 0.3681 | 0.8533 |
| 0.3441 | 0.1 | 150 | 0.2455 | 0.9 |
| 0.3052 | 0.13 | 200 | 0.3143 | 0.88 |
| 0.2991 | 0.16 | 250 | 0.1890 | 0.92 |
| 0.2954 | 0.19 | 300 | 0.2012 | 0.9267 |
| 0.2723 | 0.22 | 350 | 0.2178 | 0.9333 |
| 0.255 | 0.26 | 400 | 0.1740 | 0.9267 |
| 0.2675 | 0.29 | 450 | 0.1667 | 0.9467 |
| 0.3071 | 0.32 | 500 | 0.1766 | 0.9333 |
| 0.2498 | 0.35 | 550 | 0.1928 | 0.9267 |
| 0.2402 | 0.38 | 600 | 0.1334 | 0.94 |
| 0.2449 | 0.42 | 650 | 0.1332 | 0.9467 |
| 0.2298 | 0.45 | 700 | 0.1375 | 0.9333 |
| 0.2625 | 0.48 | 750 | 0.1529 | 0.9467 |
| 0.2459 | 0.51 | 800 | 0.1621 | 0.94 |
| 0.2499 | 0.54 | 850 | 0.1606 | 0.92 |
| 0.2405 | 0.58 | 900 | 0.1375 | 0.94 |
| 0.208 | 0.61 | 950 | 0.1697 | 0.94 |
| 0.2642 | 0.64 | 1000 | 0.1507 | 0.9467 |
| 0.2272 | 0.67 | 1050 | 0.1478 | 0.94 |
| 0.2769 | 0.7 | 1100 | 0.1423 | 0.9467 |
| 0.2293 | 0.74 | 1150 | 0.1434 | 0.9467 |
| 0.2212 | 0.77 | 1200 | 0.1371 | 0.9533 |
| 0.2176 | 0.8 | 1250 | 0.1380 | 0.9533 |
| 0.2269 | 0.83 | 1300 | 0.1453 | 0.9467 |
| 0.2422 | 0.86 | 1350 | 0.1450 | 0.9467 |
| 0.2141 | 0.9 | 1400 | 0.1775 | 0.9467 |
| 0.235 | 0.93 | 1450 | 0.1302 | 0.9467 |
| 0.2275 | 0.96 | 1500 | 0.1304 | 0.9467 |
| 0.2282 | 0.99 | 1550 | 0.1620 | 0.9533 |
| 0.1898 | 1.02 | 1600 | 0.1482 | 0.9333 |
| 0.1677 | 1.06 | 1650 | 0.1304 | 0.9533 |
| 0.1533 | 1.09 | 1700 | 0.1270 | 0.96 |
| 0.1915 | 1.12 | 1750 | 0.1601 | 0.9533 |
| 0.1687 | 1.15 | 1800 | 0.1515 | 0.9467 |
| 0.1605 | 1.18 | 1850 | 0.1729 | 0.9467 |
| 0.1731 | 1.22 | 1900 | 0.1529 | 0.94 |
| 0.1308 | 1.25 | 1950 | 0.1577 | 0.96 |
| 0.1792 | 1.28 | 2000 | 0.1668 | 0.9333 |
| 0.1987 | 1.31 | 2050 | 0.1613 | 0.9533 |
| 0.1782 | 1.34 | 2100 | 0.1542 | 0.96 |
| 0.199 | 1.38 | 2150 | 0.1437 | 0.9533 |
| 0.1224 | 1.41 | 2200 | 0.1674 | 0.96 |
| 0.1854 | 1.44 | 2250 | 0.1831 | 0.9533 |
| 0.1622 | 1.47 | 2300 | 0.1403 | 0.9533 |
| 0.1586 | 1.5 | 2350 | 0.1417 | 0.96 |
| 0.1375 | 1.54 | 2400 | 0.1409 | 0.9533 |
| 0.1401 | 1.57 | 2450 | 0.1759 | 0.96 |
| 0.1999 | 1.6 | 2500 | 0.1172 | 0.96 |
| 0.1746 | 1.63 | 2550 | 0.1479 | 0.96 |
| 0.1983 | 1.66 | 2600 | 0.1498 | 0.9467 |
| 0.1658 | 1.7 | 2650 | 0.1375 | 0.9533 |
| 0.1492 | 1.73 | 2700 | 0.1504 | 0.9667 |
| 0.1435 | 1.76 | 2750 | 0.1340 | 0.9667 |
| 0.1473 | 1.79 | 2800 | 0.1262 | 0.9667 |
| 0.1692 | 1.82 | 2850 | 0.1323 | 0.9533 |
| 0.1567 | 1.86 | 2900 | 0.1339 | 0.96 |
| 0.1615 | 1.89 | 2950 | 0.1204 | 0.9667 |
| 0.1677 | 1.92 | 3000 | 0.1202 | 0.9667 |
| 0.1426 | 1.95 | 3050 | 0.1310 | 0.96 |
| 0.1754 | 1.98 | 3100 | 0.1469 | 0.9533 |
| 0.1395 | 2.02 | 3150 | 0.1663 | 0.96 |
| 0.0702 | 2.05 | 3200 | 0.1399 | 0.9733 |
| 0.1351 | 2.08 | 3250 | 0.1520 | 0.9667 |
| 0.1194 | 2.11 | 3300 | 0.1410 | 0.9667 |
| 0.1087 | 2.14 | 3350 | 0.1361 | 0.9733 |
| 0.1245 | 2.18 | 3400 | 0.1490 | 0.9533 |
| 0.1285 | 2.21 | 3450 | 0.1799 | 0.96 |
| 0.0801 | 2.24 | 3500 | 0.1776 | 0.9533 |
| 0.117 | 2.27 | 3550 | 0.1756 | 0.9667 |
| 0.1105 | 2.3 | 3600 | 0.1749 | 0.9533 |
| 0.1359 | 2.34 | 3650 | 0.1750 | 0.96 |
| 0.1328 | 2.37 | 3700 | 0.1857 | 0.9533 |
| 0.1201 | 2.4 | 3750 | 0.1834 | 0.9533 |
| 0.1239 | 2.43 | 3800 | 0.1923 | 0.9533 |
| 0.0998 | 2.46 | 3850 | 0.1882 | 0.9533 |
| 0.0907 | 2.5 | 3900 | 0.1722 | 0.96 |
| 0.1214 | 2.53 | 3950 | 0.1787 | 0.96 |
| 0.0858 | 2.56 | 4000 | 0.1927 | 0.96 |
| 0.1384 | 2.59 | 4050 | 0.1312 | 0.96 |
| 0.0951 | 2.62 | 4100 | 0.1348 | 0.96 |
| 0.1325 | 2.66 | 4150 | 0.1652 | 0.9533 |
| 0.1429 | 2.69 | 4200 | 0.1603 | 0.9533 |
| 0.0923 | 2.72 | 4250 | 0.2141 | 0.94 |
| 0.1336 | 2.75 | 4300 | 0.1348 | 0.9733 |
| 0.0893 | 2.78 | 4350 | 0.1356 | 0.9667 |
| 0.1057 | 2.82 | 4400 | 0.1932 | 0.9533 |
| 0.0928 | 2.85 | 4450 | 0.1868 | 0.9533 |
| 0.0586 | 2.88 | 4500 | 0.1620 | 0.96 |
| 0.1426 | 2.91 | 4550 | 0.1944 | 0.9533 |
| 0.1394 | 2.94 | 4600 | 0.1630 | 0.96 |
| 0.0785 | 2.98 | 4650 | 0.1560 | 0.9667 |
| 0.0772 | 3.01 | 4700 | 0.2093 | 0.9467 |
| 0.0565 | 3.04 | 4750 | 0.1785 | 0.96 |
| 0.0771 | 3.07 | 4800 | 0.2361 | 0.9467 |
| 0.0634 | 3.1 | 4850 | 0.1809 | 0.96 |
| 0.0847 | 3.13 | 4900 | 0.1496 | 0.9733 |
| 0.0526 | 3.17 | 4950 | 0.1620 | 0.9667 |
| 0.0796 | 3.2 | 5000 | 0.1764 | 0.9667 |
| 0.0786 | 3.23 | 5050 | 0.1798 | 0.9667 |
| 0.0531 | 3.26 | 5100 | 0.1698 | 0.9667 |
| 0.0445 | 3.29 | 5150 | 0.2088 | 0.96 |
| 0.1212 | 3.33 | 5200 | 0.1842 | 0.9533 |
| 0.0825 | 3.36 | 5250 | 0.2016 | 0.9533 |
| 0.0782 | 3.39 | 5300 | 0.1775 | 0.9533 |
| 0.0627 | 3.42 | 5350 | 0.1656 | 0.96 |
| 0.0898 | 3.45 | 5400 | 0.2331 | 0.9533 |
| 0.0882 | 3.49 | 5450 | 0.2514 | 0.9467 |
| 0.0798 | 3.52 | 5500 | 0.2090 | 0.9533 |
| 0.0474 | 3.55 | 5550 | 0.2322 | 0.96 |
| 0.0773 | 3.58 | 5600 | 0.2023 | 0.96 |
| 0.0862 | 3.61 | 5650 | 0.2247 | 0.96 |
| 0.0723 | 3.65 | 5700 | 0.2001 | 0.96 |
| 0.0549 | 3.68 | 5750 | 0.2031 | 0.9533 |
| 0.044 | 3.71 | 5800 | 0.2133 | 0.96 |
| 0.0644 | 3.74 | 5850 | 0.1876 | 0.9667 |
| 0.0868 | 3.77 | 5900 | 0.2182 | 0.9533 |
| 0.072 | 3.81 | 5950 | 0.1856 | 0.9667 |
| 0.092 | 3.84 | 6000 | 0.2120 | 0.96 |
| 0.0806 | 3.87 | 6050 | 0.2006 | 0.9533 |
| 0.0627 | 3.9 | 6100 | 0.1900 | 0.9533 |
| 0.0738 | 3.93 | 6150 | 0.1869 | 0.96 |
| 0.0667 | 3.97 | 6200 | 0.2216 | 0.96 |
| 0.0551 | 4.0 | 6250 | 0.2147 | 0.9533 |
| 0.0271 | 4.03 | 6300 | 0.2038 | 0.96 |
| 0.0763 | 4.06 | 6350 | 0.2058 | 0.96 |
| 0.0612 | 4.09 | 6400 | 0.2037 | 0.9533 |
| 0.0351 | 4.13 | 6450 | 0.2081 | 0.96 |
| 0.0265 | 4.16 | 6500 | 0.2373 | 0.9533 |
| 0.0391 | 4.19 | 6550 | 0.2264 | 0.9533 |
| 0.0609 | 4.22 | 6600 | 0.2035 | 0.9533 |
| 0.0435 | 4.25 | 6650 | 0.1989 | 0.96 |
| 0.0309 | 4.29 | 6700 | 0.2096 | 0.9667 |
| 0.064 | 4.32 | 6750 | 0.2385 | 0.9533 |
| 0.0388 | 4.35 | 6800 | 0.2071 | 0.96 |
| 0.0267 | 4.38 | 6850 | 0.2336 | 0.96 |
| 0.0433 | 4.41 | 6900 | 0.2045 | 0.9667 |
| 0.0596 | 4.45 | 6950 | 0.2013 | 0.96 |
| 0.0273 | 4.48 | 7000 | 0.2122 | 0.96 |
| 0.0559 | 4.51 | 7050 | 0.2182 | 0.96 |
| 0.0504 | 4.54 | 7100 | 0.2172 | 0.96 |
| 0.0536 | 4.57 | 7150 | 0.2406 | 0.9533 |
| 0.0624 | 4.61 | 7200 | 0.2194 | 0.9533 |
| 0.0668 | 4.64 | 7250 | 0.2156 | 0.96 |
| 0.0208 | 4.67 | 7300 | 0.2150 | 0.96 |
| 0.0436 | 4.7 | 7350 | 0.2361 | 0.9533 |
| 0.0285 | 4.73 | 7400 | 0.2175 | 0.96 |
| 0.0604 | 4.77 | 7450 | 0.2241 | 0.9467 |
| 0.0502 | 4.8 | 7500 | 0.2201 | 0.96 |
| 0.0342 | 4.83 | 7550 | 0.2232 | 0.96 |
| 0.0467 | 4.86 | 7600 | 0.2247 | 0.9533 |
| 0.0615 | 4.89 | 7650 | 0.2235 | 0.96 |
| 0.0769 | 4.93 | 7700 | 0.2302 | 0.9533 |
| 0.0451 | 4.96 | 7750 | 0.2334 | 0.9467 |
| 0.0532 | 4.99 | 7800 | 0.2316 | 0.9533 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Brona/model1 | []
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} | 0 | null | ---
license: creativeml-openrail-m
---
Waifu-Diffusion-v1-3 based StableDiffusion model with Dreambooth training on images from 3 different anime style artists. Trained to 17,000 steps using 155 total training images.
## Usage
Can be used in StableDiffusion, including the extremely popular Web UI by Automatic1111, like any other model by placing the .CKPT file in the correct directory. Please consult the documentation for your installation of StableDiffusion for more specific instructions.
Use ```"m_kgrartist"``` for kagura_tohru style, ```"m_ozdmartist"``` for ozadomi style, or ```"m_srartist"``` seero style in your prompt to invoke the style of the desired artist.
## Example images from ```"m_kgrartist"```
<table>
<tr>
<td><img src=https://i.imgur.com/SIA7g2C.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/UbBsvZo.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/kMv5MH9.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/BiYihYs.png width=100% height=100%/></td>
</tr>
</table>
## Example images from ```"m_ozdmartist"```
<table>
<tr>
<td><img src=https://i.imgur.com/t2UmHWa.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/LFrQsy6.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/DnHg1Kp.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/cXooD2r.png width=100% height=100%/></td>
</tr>
</table>
## Example images from ```"m_srartist"```
<table>
<tr>
<td><img src=https://i.imgur.com/0gsFN2H.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/aDJr8x6.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/AUafGCd.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/va246Yv.png width=100% height=100%/></td>
</tr>
</table>
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) |
Brykee/DialoGPT-medium-Morty | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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} | 10 | null | ---
tags:
- generated_from_trainer
model-index:
- name: deberta-v3-large-fever
results: []
datasets:
- copenlu/fever_gold_evidence
---
<!-- 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-fever
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5286
- eval_p: 0.8827
- eval_r: 0.8826
- eval_f1: 0.8816
- eval_runtime: 231.4062
- eval_samples_per_second: 81.264
- eval_steps_per_second: 10.16
- 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.12.1
|
Bryson575x/riceboi | []
| null | {
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} | 0 | null | ---
license: openrail
library_name: diffusers
tags:
- TPU
- JAX
- Flax
- stable-diffusion
- text-to-image
language:
- en
---
|
BumBelDumBel/ZORK-AI-TEST | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
]
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} | 9 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: xtremedistil-l6-h256-uncased-eng-only-sentiment-single-finetuned-memes
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. -->
# xtremedistil-l6-h256-uncased-eng-only-sentiment-single-finetuned-memes
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3513
- Accuracy: 0.8555
- Precision: 0.8706
- Recall: 0.8697
- F1: 0.8699
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 378 | 0.3579 | 0.8526 | 0.8685 | 0.8667 | 0.8674 |
| 0.4989 | 2.0 | 756 | 0.3368 | 0.8503 | 0.8665 | 0.8650 | 0.8645 |
| 0.3318 | 3.0 | 1134 | 0.3379 | 0.8533 | 0.8693 | 0.8675 | 0.8678 |
| 0.279 | 4.0 | 1512 | 0.3426 | 0.8555 | 0.8712 | 0.8698 | 0.8696 |
| 0.279 | 5.0 | 1890 | 0.3495 | 0.8555 | 0.8717 | 0.8698 | 0.8698 |
| 0.2471 | 6.0 | 2268 | 0.3513 | 0.8555 | 0.8706 | 0.8697 | 0.8699 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|
BumBelDumBel/ZORK_AI_SCIFI | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer"
]
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} | 14 | null | ---
language:
- ru
library_name: nemo
datasets:
- mozilla-foundation/common_voice_10_0
- SberDevices/Golos
- Russian-LibriSpeech
- SOVA-Dataset
tags:
- automatic-speech-recognition
- speech
- audio
- CTC
- Conformer
- Transformer
- pytorch
- NeMo
- hf-asr-leaderboard
- Riva
license: cc-by-4.0
model-index:
- name: stt_ru_conformer_ctc_large
results:
- task:
type: Automatic Speech Recognition
name: speech-recognition
dataset:
name: Mozilla Common Voice 10.0
type: mozilla-foundation/common_voice_10_0
config: ru
split: test
args:
language: ru
metrics:
- name: Test WER
type: wer
value: 4.28
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Mozilla Common Voice 10.0
type: mozilla-foundation/common_voice_10_0
config: ru
split: dev
args:
language: ru
metrics:
- name: Dev WER
type: wer
value: 3.94
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Sberdevices Golos (crowd)
type: SberDevices/Golos
config: crowd
split: test
args:
language: ru
metrics:
- name: Test WER
type: wer
value: 2.77
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Sberdevices Golos (farfield)
type: SberDevices/Golos
config: farfield
split: test
args:
language: ru
metrics:
- name: Test WER
type: wer
value: 7.15
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Russian LibriSpeech
type: RuLS
config: ru
split: test
args:
language: ru
metrics:
- name: Test WER
type: wer
value: 13.60
---
# NVIDIA Conformer-CTC Large (Russian)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [](#datasets)
This model transcribes speech into lowercase Cyrillic alphabet including space, and is trained on around 1636 hours of Russian speech data.
It is a non-autoregressive "large" variant of Conformer, with around 120 million parameters.
See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-ctc) for complete architecture details.
## Usage
The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest PyTorch version.
```
pip install nemo_toolkit['all']
```
### Automatically instantiate the model
```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_ru_conformer_ctc_large")
```
### Transcribing using Python
Simply do:
```
asr_model.transcribe(['<your_audio>.wav'])
```
### Transcribing many audio files
```shell
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py
pretrained_name="nvidia/stt_ru_conformer_ctc_large"
audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
```
### Input
This model accepts 16 kHz mono-channel Audio (wav files) as input.
### Output
This model provides transcribed speech as a string for a given audio sample.
## Model Architecture
Conformer-CTC model is a non-autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on the detail of this model here: [Conformer-CTC Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-ctc).
## Training
The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_ctc_bpe.yaml).
The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
The vocabulary we use contains 33 characters:
```python
[' ', 'а', 'б', 'в', 'г', 'д', 'е', 'ж', 'з', 'и', 'й', 'к', 'л', 'м', 'н', 'о', 'п', 'р', 'с', 'т', 'у', 'ф', 'х', 'ц', 'ч', 'ш', 'щ', 'ъ', 'ы', 'ь', 'э', 'ю', 'я']
```
Rare symbols with diacritics were replaced during preprocessing.
The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
### Datasets
All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of more than a thousand hours of Russian speech:
- Mozilla Common Voice 10.0 (Russian) - train subset [28 hours]
- Golos - crowd [1070 hours] and fairfield [111 hours] subsets
- Russian LibriSpeech (RuLS) [92 hours]
- SOVA - RuAudiobooksDevices [260 hours] and RuDevices [75 hours] subsets
## Performance
The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.
| Version | Tokenizer | Vocabulary Size | MCV 10.0 dev | MCV 10.0 test | GOLOS-crowd test | GOLOS-farfield test | RuLS test | Train Dataset |
|---------|-----------------------|-----------------|--------------|---------------|------------------|---------------------|-----------|---------------|
| 1.13.0 | SentencePiece Unigram | 128 | 3.94 | 4.28 | 2.77 | 7.15 | 13.60 | NeMo ASRSET |
## Limitations
Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
## Deployment with NVIDIA Riva
For the best real-time accuracy, latency, and throughput, deploy the model with [NVIDIA Riva](https://developer.nvidia.com/riva), an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, at the edge, and embedded.
Additionally, Riva provides:
* World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours
* Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization
* Streaming speech recognition, Kubernetes compatible scaling, and Enterprise-grade support
Check out [Riva live demo](https://developer.nvidia.com/riva#demos).
## References
- [1] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)
- [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)
- [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) |
BunakovD/sd | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 674.50 +/- 229.13
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bguan -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bguan -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga bguan
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Buntan/BuntanAI | []
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} | 0 | 2022-11-01T20:50:51Z | # Positive Perspectives with Text Reframing
Based on the paper [Inducing Positive Perspectives with Text Reframing](https://arxiv.org/abs/2204.02952), this model focuses on the positive reframing task. The purpose of the model is to neutralize a negative point of view and generate a more positive perspective without changing the original meaning.
The model provided is obtained from this [HuggingFace Space](https://huggingface.co/spaces/Ella2323/Positive-Reframing) and stored in a separate repository to increase ease of use. All credits go to the original contributors of the abovementioned HuggingFace Space.
### Available strategies for positive reframing:
**growth**: viewing a challenging event as an opportunity for the author to specifically grow or improve himself.
**impermanence**: Saying that bad things don't last forever, will get better soon, and/or that other people have had similar difficulties.
**neutralizing**: Replacing a negative word with a neutral word. For example, “This was a terrible day” becomes “This was a long day”.
**optimism**: Focusing on things about the situation itself, at that moment, that are good (not just predicting a better future).
**self_affirmation**: Talking about what strengths the author already has, or values he admires, such as love, courage, perseverance, etc.
**thankfulness**: Expressing gratitude or gratitude with keywords like appreciate, happy for it, grateful for, good thing, etc.
|
Buntan/xlm-roberta-base-finetuned-marc-en | []
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} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: spanbert-base-cased-LAT-True-added-tokenizer
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. -->
# spanbert-base-cased-LAT-True-added-tokenizer
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2767
## 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: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 174 | 0.3422 |
| No log | 2.0 | 348 | 0.2893 |
| 0.3406 | 3.0 | 522 | 0.2767 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 85 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_polarity
metrics:
- accuracy
model-index:
- name: amazonPolarity_roBERTa_5E
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_polarity
type: amazon_polarity
config: amazon_polarity
split: train
args: amazon_polarity
metrics:
- name: Accuracy
type: accuracy
value: 0.96
---
<!-- 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. -->
# amazonPolarity_roBERTa_5E
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the amazon_polarity dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2201
- Accuracy: 0.96
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5785 | 0.05 | 50 | 0.2706 | 0.9133 |
| 0.2731 | 0.11 | 100 | 0.2379 | 0.9267 |
| 0.2223 | 0.16 | 150 | 0.1731 | 0.92 |
| 0.1887 | 0.21 | 200 | 0.1672 | 0.9267 |
| 0.1915 | 0.27 | 250 | 0.2946 | 0.9067 |
| 0.1981 | 0.32 | 300 | 0.1744 | 0.9267 |
| 0.1617 | 0.37 | 350 | 0.2349 | 0.92 |
| 0.1919 | 0.43 | 400 | 0.1605 | 0.9333 |
| 0.1713 | 0.48 | 450 | 0.1626 | 0.94 |
| 0.1961 | 0.53 | 500 | 0.1555 | 0.9467 |
| 0.1652 | 0.59 | 550 | 0.1996 | 0.94 |
| 0.1719 | 0.64 | 600 | 0.1848 | 0.9333 |
| 0.159 | 0.69 | 650 | 0.1783 | 0.9467 |
| 0.1533 | 0.75 | 700 | 0.2016 | 0.9467 |
| 0.1749 | 0.8 | 750 | 0.3943 | 0.8733 |
| 0.1675 | 0.85 | 800 | 0.1948 | 0.9133 |
| 0.1601 | 0.91 | 850 | 0.2044 | 0.92 |
| 0.1424 | 0.96 | 900 | 0.1061 | 0.9533 |
| 0.1447 | 1.01 | 950 | 0.2195 | 0.9267 |
| 0.0997 | 1.07 | 1000 | 0.2102 | 0.9333 |
| 0.1454 | 1.12 | 1050 | 0.1648 | 0.9467 |
| 0.1326 | 1.17 | 1100 | 0.2774 | 0.9 |
| 0.1192 | 1.23 | 1150 | 0.1337 | 0.96 |
| 0.1429 | 1.28 | 1200 | 0.1451 | 0.96 |
| 0.1227 | 1.33 | 1250 | 0.1995 | 0.94 |
| 0.1343 | 1.39 | 1300 | 0.2115 | 0.92 |
| 0.1208 | 1.44 | 1350 | 0.1832 | 0.9467 |
| 0.1314 | 1.49 | 1400 | 0.1298 | 0.96 |
| 0.1069 | 1.55 | 1450 | 0.1778 | 0.94 |
| 0.126 | 1.6 | 1500 | 0.1205 | 0.9667 |
| 0.1162 | 1.65 | 1550 | 0.1569 | 0.9533 |
| 0.0961 | 1.71 | 1600 | 0.1865 | 0.9467 |
| 0.13 | 1.76 | 1650 | 0.1458 | 0.96 |
| 0.1206 | 1.81 | 1700 | 0.1648 | 0.96 |
| 0.1096 | 1.87 | 1750 | 0.2221 | 0.9333 |
| 0.1138 | 1.92 | 1800 | 0.1727 | 0.9533 |
| 0.1258 | 1.97 | 1850 | 0.2036 | 0.9467 |
| 0.1032 | 2.03 | 1900 | 0.1710 | 0.9667 |
| 0.082 | 2.08 | 1950 | 0.2380 | 0.9467 |
| 0.101 | 2.13 | 2000 | 0.1868 | 0.9533 |
| 0.0913 | 2.19 | 2050 | 0.2934 | 0.9267 |
| 0.0859 | 2.24 | 2100 | 0.2385 | 0.9333 |
| 0.1019 | 2.29 | 2150 | 0.1697 | 0.9667 |
| 0.1069 | 2.35 | 2200 | 0.1815 | 0.94 |
| 0.0805 | 2.4 | 2250 | 0.2185 | 0.9467 |
| 0.0906 | 2.45 | 2300 | 0.1923 | 0.96 |
| 0.105 | 2.51 | 2350 | 0.1720 | 0.96 |
| 0.0866 | 2.56 | 2400 | 0.1710 | 0.96 |
| 0.0821 | 2.61 | 2450 | 0.2267 | 0.9533 |
| 0.107 | 2.67 | 2500 | 0.2203 | 0.9467 |
| 0.0841 | 2.72 | 2550 | 0.1621 | 0.9533 |
| 0.0811 | 2.77 | 2600 | 0.1954 | 0.9533 |
| 0.1077 | 2.83 | 2650 | 0.2107 | 0.9533 |
| 0.0771 | 2.88 | 2700 | 0.2398 | 0.9467 |
| 0.08 | 2.93 | 2750 | 0.1816 | 0.96 |
| 0.0827 | 2.99 | 2800 | 0.2311 | 0.9467 |
| 0.1118 | 3.04 | 2850 | 0.1825 | 0.96 |
| 0.0626 | 3.09 | 2900 | 0.2876 | 0.9333 |
| 0.0733 | 3.14 | 2950 | 0.2045 | 0.9467 |
| 0.0554 | 3.2 | 3000 | 0.1775 | 0.96 |
| 0.0569 | 3.25 | 3050 | 0.2208 | 0.9467 |
| 0.0566 | 3.3 | 3100 | 0.2113 | 0.9533 |
| 0.063 | 3.36 | 3150 | 0.2013 | 0.96 |
| 0.056 | 3.41 | 3200 | 0.2229 | 0.96 |
| 0.0791 | 3.46 | 3250 | 0.2472 | 0.9467 |
| 0.0867 | 3.52 | 3300 | 0.1630 | 0.9667 |
| 0.0749 | 3.57 | 3350 | 0.2066 | 0.9533 |
| 0.0653 | 3.62 | 3400 | 0.2085 | 0.96 |
| 0.0784 | 3.68 | 3450 | 0.2068 | 0.9467 |
| 0.074 | 3.73 | 3500 | 0.1976 | 0.96 |
| 0.076 | 3.78 | 3550 | 0.1953 | 0.9533 |
| 0.0807 | 3.84 | 3600 | 0.2246 | 0.9467 |
| 0.077 | 3.89 | 3650 | 0.1867 | 0.9533 |
| 0.0771 | 3.94 | 3700 | 0.2035 | 0.9533 |
| 0.0658 | 4.0 | 3750 | 0.1754 | 0.9667 |
| 0.0711 | 4.05 | 3800 | 0.1977 | 0.9667 |
| 0.066 | 4.1 | 3850 | 0.1806 | 0.9667 |
| 0.0627 | 4.16 | 3900 | 0.1819 | 0.96 |
| 0.0671 | 4.21 | 3950 | 0.2247 | 0.9533 |
| 0.0245 | 4.26 | 4000 | 0.2482 | 0.9467 |
| 0.0372 | 4.32 | 4050 | 0.2201 | 0.96 |
| 0.0607 | 4.37 | 4100 | 0.2381 | 0.9467 |
| 0.0689 | 4.42 | 4150 | 0.2159 | 0.96 |
| 0.0383 | 4.48 | 4200 | 0.2278 | 0.9533 |
| 0.0382 | 4.53 | 4250 | 0.2277 | 0.96 |
| 0.0626 | 4.58 | 4300 | 0.2325 | 0.96 |
| 0.0595 | 4.64 | 4350 | 0.2315 | 0.96 |
| 0.0578 | 4.69 | 4400 | 0.2284 | 0.96 |
| 0.0324 | 4.74 | 4450 | 0.2297 | 0.96 |
| 0.0476 | 4.8 | 4500 | 0.2154 | 0.96 |
| 0.0309 | 4.85 | 4550 | 0.2258 | 0.96 |
| 0.0748 | 4.9 | 4600 | 0.2131 | 0.96 |
| 0.0731 | 4.96 | 4650 | 0.2201 | 0.96 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
}
} | 42 | null | ---
license: openrail
library_name: diffusers
tags:
- TPU
- JAX
- Flax
- stable-diffusion
- text-to-image
language:
- en
---
|
CAMeL-Lab/bert-base-arabic-camelbert-da-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
} | 37 | 2022-11-01T21:42:16Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# AlekseyKorshuk/retriever-coding-guru-adapted
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('AlekseyKorshuk/retriever-coding-guru-adapted')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('AlekseyKorshuk/retriever-coding-guru-adapted')
model = AutoModel.from_pretrained('AlekseyKorshuk/retriever-coding-guru-adapted')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=AlekseyKorshuk/retriever-coding-guru-adapted)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 317 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 31,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
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}
}
} | 54 | null | ---
language: ro
inference: false
license: apache-2.0
---
This is a pretrained-from-scratch **T5v1.1 base** model (**247M** parameters) on the [t5x](https://github.com/google-research/t5x) platform.
Training was performed on a clean 80GB Romanian text corpus for 4M steps with these [scripts](https://github.com/dumitrescustefan/t5x_models). The model was trained with an encoder sequence length of 512 and a decoder sequence length of 256.
**!! IMPORTANT !!** This model was pretrained on the span corruption MLM task, meaning this model is **not usable** in any downstream task **without finetuning** first!
### How to load a t5x model
```python
from transformers import T5Tokenizer, T5Model
tokenizer = T5Tokenizer.from_pretrained('dumitrescustefan/t5-v1_1-base-romanian')
model = T5Model.from_pretrained('dumitrescustefan/t5-v1_1-base-romanian')
input_ids = tokenizer("Acesta este un test", return_tensors="pt").input_ids # Batch size 1
decoder_input_ids = tokenizer("Acesta este", return_tensors="pt").input_ids # Batch size 1
# preprocess: Prepend decoder_input_ids with start token which is pad token for T5Model.
# This is not needed for torch's T5ForConditionalGeneration as it does this internally using labels arg.
decoder_input_ids = model._shift_right(decoder_input_ids)
# forward pass
outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
last_hidden_states = outputs.last_hidden_state
print(last_hidden_states.shape) # this will print [1, 3, 768]
```
Remember to always sanitize your text! Replace ``ş`` and ``ţ`` cedilla-letters to comma-letters with :
```python
text = text.replace("ţ", "ț").replace("ş", "ș").replace("Ţ", "Ț").replace("Ş", "Ș")
```
because the model was **not** trained on cedilla ``ş`` and ``ţ``s. If you don't, you will have decreased performance due to ``<UNK>``s and increased number of tokens per word.
### Acknowledgements
We'd like to thank [TPU Research Cloud](https://sites.research.google/trc/about/) for providing the TPUv4 cores we used to train these models!
### Authors
Yours truly,
_[Stefan Dumitrescu](https://github.com/dumitrescustefan), [Mihai Ilie](https://github.com/iliemihai) and [Per Egil Kummervold](https://huggingface.co/north)_
|
CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"has_space"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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}
}
} | 19,850 | null | ---
tags:
- image-classification
- timm
- vision
library_tag: timm
license: apache-2.0
---
# CLIP (OpenAI model for timm)
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.
This instance of the CLIP model is intended for loading in
* `timm` (https://github.com/rwightman/pytorch-image-models) and
* `OpenCLIP` (https://github.com/mlfoundations/open_clip) libraries.
Please see https://huggingface.co/openai/clip-vit-base-patch16 for use in Hugging Face Transformers.
### Model Date
January 2021
### Model Type
The model uses a ViT-B/16 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss.
The original implementation had two variants: one using a ResNet image encoder and the other using a Vision Transformer. This repository has the variant with the Vision Transformer.
### Documents
- [Blog Post](https://openai.com/blog/clip/)
- [CLIP Paper](https://arxiv.org/abs/2103.00020)
## Model Use
### Intended Use
The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
#### Primary intended uses
The primary intended users of these models are AI researchers.
We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.
### Out-of-Scope Use Cases
**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful.
Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
## Data
The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users.
### Data Mission Statement
Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset.
## Limitations
CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance.
### Bias and Fairness
We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper).
We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with ‘Middle Eastern’ having the highest accuracy (98.4%) and ‘White’ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks.
|
CAMeL-Lab/bert-base-arabic-camelbert-da | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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} | 449 | null | ---
tags:
- timm
- vision
library_tag: timm
license: apache-2.0
---
# CLIP (OpenAI model for timm)
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.
This instance of the CLIP model is intended for loading in
* `timm` (https://github.com/rwightman/pytorch-image-models) and
* `OpenCLIP` (https://github.com/mlfoundations/open_clip) libraries.
Please see https://huggingface.co/openai/clip-vit-base-patch32 for use in Hugging Face Transformers.
### Model Date
January 2021
### Model Type
The model uses a ViT-B/32 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss.
The original implementation had two variants: one using a ResNet image encoder and the other using a Vision Transformer. This repository has the variant with the Vision Transformer.
### Documents
- [Blog Post](https://openai.com/blog/clip/)
- [CLIP Paper](https://arxiv.org/abs/2103.00020)
## Model Use
### Intended Use
The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
#### Primary intended uses
The primary intended users of these models are AI researchers.
We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.
### Out-of-Scope Use Cases
**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful.
Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
## Data
The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users.
### Data Mission Statement
Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset.
## Limitations
CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance.
### Bias and Fairness
We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper).
We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with ‘Middle Eastern’ having the highest accuracy (98.4%) and ‘White’ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks.
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus26 | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
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}
}
} | 45 | null | ---
tags:
- timm
- vision
library_tag: timm
license: apache-2.0
---
# CLIP (OpenAI model for timm)
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.
This instance of the CLIP model is intended for loading in
* `timm` (https://github.com/rwightman/pytorch-image-models) and
* `OpenCLIP` (https://github.com/mlfoundations/open_clip) libraries.
Please see https://huggingface.co/openai/clip-vit-large-patch14 for use in Hugging Face Transformers.
### Model Date
January 2021
### Model Type
The model uses a ViT-L/14 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss.
The original implementation had two variants: one using a ResNet image encoder and the other using a Vision Transformer. This repository has the variant with the Vision Transformer.
### Documents
- [Blog Post](https://openai.com/blog/clip/)
- [CLIP Paper](https://arxiv.org/abs/2103.00020)
## Model Use
### Intended Use
The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
#### Primary intended uses
The primary intended users of these models are AI researchers.
We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.
### Out-of-Scope Use Cases
**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful.
Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
## Data
The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users.
### Data Mission Statement
Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset.
## Limitations
CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance.
### Bias and Fairness
We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper).
We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with ‘Middle Eastern’ having the highest accuracy (98.4%) and ‘White’ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks.
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
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}
} | 31 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_polarity
metrics:
- accuracy
model-index:
- name: amazonPolarity_BERT_5E
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_polarity
type: amazon_polarity
config: amazon_polarity
split: train
args: amazon_polarity
metrics:
- name: Accuracy
type: accuracy
value: 0.9066666666666666
---
<!-- 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. -->
# amazonPolarity_BERT_5E
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the amazon_polarity dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4402
- Accuracy: 0.9067
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7011 | 0.03 | 50 | 0.6199 | 0.7 |
| 0.6238 | 0.05 | 100 | 0.4710 | 0.8133 |
| 0.4478 | 0.08 | 150 | 0.3249 | 0.8733 |
| 0.3646 | 0.11 | 200 | 0.3044 | 0.86 |
| 0.3244 | 0.13 | 250 | 0.2548 | 0.86 |
| 0.2734 | 0.16 | 300 | 0.2666 | 0.88 |
| 0.2784 | 0.19 | 350 | 0.2416 | 0.88 |
| 0.2706 | 0.21 | 400 | 0.2660 | 0.88 |
| 0.2368 | 0.24 | 450 | 0.2522 | 0.8867 |
| 0.2449 | 0.27 | 500 | 0.3135 | 0.88 |
| 0.262 | 0.29 | 550 | 0.2718 | 0.8733 |
| 0.2111 | 0.32 | 600 | 0.2494 | 0.8933 |
| 0.2459 | 0.35 | 650 | 0.2468 | 0.8867 |
| 0.2264 | 0.37 | 700 | 0.3049 | 0.8667 |
| 0.2572 | 0.4 | 750 | 0.2054 | 0.8933 |
| 0.1749 | 0.43 | 800 | 0.3489 | 0.86 |
| 0.2423 | 0.45 | 850 | 0.2142 | 0.8933 |
| 0.1931 | 0.48 | 900 | 0.2096 | 0.9067 |
| 0.2444 | 0.51 | 950 | 0.3404 | 0.8733 |
| 0.2666 | 0.53 | 1000 | 0.2378 | 0.9067 |
| 0.2311 | 0.56 | 1050 | 0.2416 | 0.9067 |
| 0.2269 | 0.59 | 1100 | 0.3188 | 0.8733 |
| 0.2143 | 0.61 | 1150 | 0.2343 | 0.9 |
| 0.2181 | 0.64 | 1200 | 0.2606 | 0.8667 |
| 0.2151 | 0.67 | 1250 | 0.1888 | 0.9133 |
| 0.2694 | 0.69 | 1300 | 0.3982 | 0.8467 |
| 0.2408 | 0.72 | 1350 | 0.1978 | 0.9067 |
| 0.2043 | 0.75 | 1400 | 0.2125 | 0.9 |
| 0.2081 | 0.77 | 1450 | 0.2680 | 0.8933 |
| 0.2361 | 0.8 | 1500 | 0.3723 | 0.8467 |
| 0.2503 | 0.83 | 1550 | 0.3427 | 0.8733 |
| 0.1983 | 0.85 | 1600 | 0.2525 | 0.9067 |
| 0.1947 | 0.88 | 1650 | 0.2427 | 0.9133 |
| 0.2411 | 0.91 | 1700 | 0.2448 | 0.9 |
| 0.2381 | 0.93 | 1750 | 0.3354 | 0.88 |
| 0.1852 | 0.96 | 1800 | 0.3078 | 0.8667 |
| 0.2427 | 0.99 | 1850 | 0.2408 | 0.9 |
| 0.1582 | 1.01 | 1900 | 0.2698 | 0.9133 |
| 0.159 | 1.04 | 1950 | 0.3383 | 0.9 |
| 0.1833 | 1.07 | 2000 | 0.2849 | 0.9 |
| 0.1257 | 1.09 | 2050 | 0.5376 | 0.8667 |
| 0.1513 | 1.12 | 2100 | 0.4469 | 0.88 |
| 0.1869 | 1.15 | 2150 | 0.3415 | 0.8933 |
| 0.1342 | 1.17 | 2200 | 0.3021 | 0.8867 |
| 0.1404 | 1.2 | 2250 | 0.3619 | 0.88 |
| 0.1576 | 1.23 | 2300 | 0.2815 | 0.9 |
| 0.1419 | 1.25 | 2350 | 0.4351 | 0.8867 |
| 0.1491 | 1.28 | 2400 | 0.3025 | 0.9133 |
| 0.1914 | 1.31 | 2450 | 0.3011 | 0.9067 |
| 0.1265 | 1.33 | 2500 | 0.3953 | 0.88 |
| 0.128 | 1.36 | 2550 | 0.2557 | 0.9333 |
| 0.1631 | 1.39 | 2600 | 0.2226 | 0.9333 |
| 0.1019 | 1.41 | 2650 | 0.3638 | 0.9133 |
| 0.1551 | 1.44 | 2700 | 0.3591 | 0.9 |
| 0.1853 | 1.47 | 2750 | 0.5005 | 0.8733 |
| 0.1578 | 1.49 | 2800 | 0.2662 | 0.92 |
| 0.1522 | 1.52 | 2850 | 0.2545 | 0.9267 |
| 0.1188 | 1.55 | 2900 | 0.3874 | 0.88 |
| 0.1638 | 1.57 | 2950 | 0.3003 | 0.92 |
| 0.1583 | 1.6 | 3000 | 0.2702 | 0.92 |
| 0.1844 | 1.63 | 3050 | 0.2183 | 0.9333 |
| 0.1365 | 1.65 | 3100 | 0.3322 | 0.8933 |
| 0.1683 | 1.68 | 3150 | 0.2069 | 0.9467 |
| 0.168 | 1.71 | 3200 | 0.4046 | 0.8667 |
| 0.1907 | 1.73 | 3250 | 0.3411 | 0.8933 |
| 0.1695 | 1.76 | 3300 | 0.1992 | 0.9333 |
| 0.1851 | 1.79 | 3350 | 0.2370 | 0.92 |
| 0.1302 | 1.81 | 3400 | 0.3058 | 0.9133 |
| 0.1353 | 1.84 | 3450 | 0.3134 | 0.9067 |
| 0.1428 | 1.87 | 3500 | 0.3767 | 0.8667 |
| 0.1642 | 1.89 | 3550 | 0.3239 | 0.8867 |
| 0.1319 | 1.92 | 3600 | 0.4725 | 0.86 |
| 0.1714 | 1.95 | 3650 | 0.3115 | 0.8867 |
| 0.1265 | 1.97 | 3700 | 0.3621 | 0.8867 |
| 0.1222 | 2.0 | 3750 | 0.3665 | 0.8933 |
| 0.0821 | 2.03 | 3800 | 0.2482 | 0.9133 |
| 0.1136 | 2.05 | 3850 | 0.3244 | 0.9 |
| 0.0915 | 2.08 | 3900 | 0.4745 | 0.8733 |
| 0.0967 | 2.11 | 3950 | 0.2346 | 0.94 |
| 0.0962 | 2.13 | 4000 | 0.3139 | 0.92 |
| 0.1001 | 2.16 | 4050 | 0.2944 | 0.9267 |
| 0.086 | 2.19 | 4100 | 0.5542 | 0.86 |
| 0.0588 | 2.21 | 4150 | 0.4377 | 0.9 |
| 0.1056 | 2.24 | 4200 | 0.3540 | 0.9133 |
| 0.0899 | 2.27 | 4250 | 0.5661 | 0.8733 |
| 0.0737 | 2.29 | 4300 | 0.5683 | 0.8733 |
| 0.1152 | 2.32 | 4350 | 0.2997 | 0.9333 |
| 0.0852 | 2.35 | 4400 | 0.5055 | 0.8933 |
| 0.1114 | 2.37 | 4450 | 0.3099 | 0.92 |
| 0.0821 | 2.4 | 4500 | 0.3026 | 0.9267 |
| 0.0698 | 2.43 | 4550 | 0.3250 | 0.92 |
| 0.1123 | 2.45 | 4600 | 0.3674 | 0.9 |
| 0.1196 | 2.48 | 4650 | 0.4539 | 0.8733 |
| 0.0617 | 2.51 | 4700 | 0.3446 | 0.92 |
| 0.0939 | 2.53 | 4750 | 0.3302 | 0.92 |
| 0.1114 | 2.56 | 4800 | 0.5149 | 0.8733 |
| 0.1154 | 2.59 | 4850 | 0.4935 | 0.8867 |
| 0.1495 | 2.61 | 4900 | 0.4706 | 0.8933 |
| 0.0858 | 2.64 | 4950 | 0.4048 | 0.9 |
| 0.0767 | 2.67 | 5000 | 0.3849 | 0.9133 |
| 0.0569 | 2.69 | 5050 | 0.5491 | 0.8867 |
| 0.1058 | 2.72 | 5100 | 0.5872 | 0.8733 |
| 0.0899 | 2.75 | 5150 | 0.3159 | 0.92 |
| 0.0757 | 2.77 | 5200 | 0.5861 | 0.8733 |
| 0.1305 | 2.8 | 5250 | 0.3633 | 0.9133 |
| 0.1027 | 2.83 | 5300 | 0.3972 | 0.9133 |
| 0.1259 | 2.85 | 5350 | 0.4197 | 0.8933 |
| 0.1255 | 2.88 | 5400 | 0.4583 | 0.8867 |
| 0.0981 | 2.91 | 5450 | 0.4657 | 0.8933 |
| 0.0736 | 2.93 | 5500 | 0.4036 | 0.9133 |
| 0.116 | 2.96 | 5550 | 0.3026 | 0.9067 |
| 0.0692 | 2.99 | 5600 | 0.3409 | 0.9133 |
| 0.0721 | 3.01 | 5650 | 0.5598 | 0.8733 |
| 0.052 | 3.04 | 5700 | 0.4130 | 0.9133 |
| 0.0661 | 3.07 | 5750 | 0.2589 | 0.9333 |
| 0.0667 | 3.09 | 5800 | 0.4484 | 0.9067 |
| 0.0599 | 3.12 | 5850 | 0.4883 | 0.9 |
| 0.0406 | 3.15 | 5900 | 0.4516 | 0.9067 |
| 0.0837 | 3.17 | 5950 | 0.3394 | 0.9267 |
| 0.0636 | 3.2 | 6000 | 0.4649 | 0.8867 |
| 0.0861 | 3.23 | 6050 | 0.5046 | 0.8933 |
| 0.0667 | 3.25 | 6100 | 0.3252 | 0.92 |
| 0.0401 | 3.28 | 6150 | 0.2771 | 0.94 |
| 0.0998 | 3.31 | 6200 | 0.4509 | 0.9 |
| 0.0209 | 3.33 | 6250 | 0.4666 | 0.8933 |
| 0.0747 | 3.36 | 6300 | 0.5430 | 0.8867 |
| 0.0678 | 3.39 | 6350 | 0.4050 | 0.9067 |
| 0.0685 | 3.41 | 6400 | 0.3738 | 0.92 |
| 0.0654 | 3.44 | 6450 | 0.4486 | 0.9 |
| 0.0496 | 3.47 | 6500 | 0.4386 | 0.9067 |
| 0.0379 | 3.49 | 6550 | 0.4547 | 0.9067 |
| 0.0897 | 3.52 | 6600 | 0.4197 | 0.9133 |
| 0.0729 | 3.55 | 6650 | 0.2855 | 0.9333 |
| 0.0515 | 3.57 | 6700 | 0.4459 | 0.9067 |
| 0.0588 | 3.6 | 6750 | 0.3627 | 0.92 |
| 0.0724 | 3.63 | 6800 | 0.4060 | 0.9267 |
| 0.0607 | 3.65 | 6850 | 0.4505 | 0.9133 |
| 0.0252 | 3.68 | 6900 | 0.5465 | 0.8933 |
| 0.0594 | 3.71 | 6950 | 0.4786 | 0.9067 |
| 0.0743 | 3.73 | 7000 | 0.4163 | 0.9267 |
| 0.0506 | 3.76 | 7050 | 0.3801 | 0.92 |
| 0.0548 | 3.79 | 7100 | 0.3557 | 0.9267 |
| 0.0932 | 3.81 | 7150 | 0.4278 | 0.9133 |
| 0.0643 | 3.84 | 7200 | 0.4673 | 0.9 |
| 0.0631 | 3.87 | 7250 | 0.3611 | 0.92 |
| 0.0793 | 3.89 | 7300 | 0.3956 | 0.9067 |
| 0.0729 | 3.92 | 7350 | 0.6630 | 0.8733 |
| 0.0552 | 3.95 | 7400 | 0.4259 | 0.8867 |
| 0.0432 | 3.97 | 7450 | 0.3615 | 0.92 |
| 0.0697 | 4.0 | 7500 | 0.5116 | 0.88 |
| 0.0463 | 4.03 | 7550 | 0.3334 | 0.94 |
| 0.046 | 4.05 | 7600 | 0.4704 | 0.8867 |
| 0.0371 | 4.08 | 7650 | 0.3323 | 0.94 |
| 0.0809 | 4.11 | 7700 | 0.3503 | 0.92 |
| 0.0285 | 4.13 | 7750 | 0.3360 | 0.92 |
| 0.0469 | 4.16 | 7800 | 0.3365 | 0.9333 |
| 0.041 | 4.19 | 7850 | 0.5726 | 0.88 |
| 0.0447 | 4.21 | 7900 | 0.4564 | 0.9067 |
| 0.0144 | 4.24 | 7950 | 0.5521 | 0.8867 |
| 0.0511 | 4.27 | 8000 | 0.5661 | 0.88 |
| 0.0481 | 4.29 | 8050 | 0.3445 | 0.94 |
| 0.036 | 4.32 | 8100 | 0.3247 | 0.94 |
| 0.0662 | 4.35 | 8150 | 0.3647 | 0.9333 |
| 0.051 | 4.37 | 8200 | 0.5024 | 0.9 |
| 0.0546 | 4.4 | 8250 | 0.4737 | 0.8933 |
| 0.0526 | 4.43 | 8300 | 0.4067 | 0.92 |
| 0.0291 | 4.45 | 8350 | 0.3862 | 0.9267 |
| 0.0292 | 4.48 | 8400 | 0.5101 | 0.9 |
| 0.0426 | 4.51 | 8450 | 0.4207 | 0.92 |
| 0.0771 | 4.53 | 8500 | 0.5525 | 0.8867 |
| 0.0668 | 4.56 | 8550 | 0.4487 | 0.9067 |
| 0.0585 | 4.59 | 8600 | 0.3574 | 0.9267 |
| 0.0375 | 4.61 | 8650 | 0.3980 | 0.92 |
| 0.0508 | 4.64 | 8700 | 0.4064 | 0.92 |
| 0.0334 | 4.67 | 8750 | 0.3031 | 0.94 |
| 0.0257 | 4.69 | 8800 | 0.3340 | 0.9333 |
| 0.0165 | 4.72 | 8850 | 0.4011 | 0.92 |
| 0.0553 | 4.75 | 8900 | 0.4243 | 0.9133 |
| 0.0597 | 4.77 | 8950 | 0.3685 | 0.9267 |
| 0.0407 | 4.8 | 9000 | 0.4262 | 0.9133 |
| 0.032 | 4.83 | 9050 | 0.4080 | 0.9133 |
| 0.0573 | 4.85 | 9100 | 0.4416 | 0.9133 |
| 0.0308 | 4.88 | 9150 | 0.4397 | 0.9133 |
| 0.0494 | 4.91 | 9200 | 0.4476 | 0.9067 |
| 0.015 | 4.93 | 9250 | 0.4419 | 0.9067 |
| 0.0443 | 4.96 | 9300 | 0.4347 | 0.9133 |
| 0.0479 | 4.99 | 9350 | 0.4402 | 0.9067 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
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}
} | 132 | 2022-11-01T22:36:20Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-base_question_generation
results: []
---
# BART-base Question Generation
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on different questions and answering dataset. It was trained to generation question using two different approaches, <b> Casual-Generation </b> and <b> Context-based-Generation </b>.
## Model description
The model takes context as an input sequence, and will generate a full question sentence as an output sequence. There are two ways the model can be queried produce the questions:
- <b> Casual-Generation </b>: where the model is tasked to generate questions answerable by a given passage. The input should be follow the structure or format: '\<generate_questions\> paragraph: put your passage text here'. <br/>
Example: <br/>
\<generate_questions\> paragraph: The lithosphere is broken into tectonic plates whose motion allows heat to escape from the interior of the Earth into space. The crust lies on top of the mantle, a configuration that is stable because the upper mantle is made of peridotite and is therefore significantly denser than the crust. The boundary between the crust and mantle is conventionally placed at the Mohorovičić discontinuity, a boundary defined by a contrast in seismic velocity.
- <b> Context-based-Generation </b>: given a section of a passage (context), the model is tasked to generate questions from the passage about the selected section or context. The input should be follow the structure or format: \<generate_context_questions\> \<section\> put your context here \</section\> paragraph: put your passage text here'. <br/>
Example: <br/>
\<generate_context_questions\> \<section\> Mohorovičić discontinuity \</section\> paragraph: The lithosphere is broken into tectonic plates whose motion allows heat to escape from the interior of the Earth into space. The crust lies on top of the mantle, a configuration that is stable because the upper mantle is made of peridotite and is therefore significantly denser than the crust. The boundary between the crust and mantle is conventionally placed at the Mohorovičić discontinuity, a boundary defined by a contrast in seismic velocity.
The input sequence can then be encoded and passed as the input_ids argument in the model's generate() method.
## limitations
The model was trained on only a limited amount of data hence questions might be poor quality. In addition the questions generated have style similar to that of the training data.
## Training and evaluation data
The dataset used to train the model comprises the training datasets from:
- Reasoning Over Paragraph Effects in Situations (ROPES): https://allenai.org/data/ropes
- SQUAD:
- DROP (Discrete Reasoning Over Paragraphs): https://allenai.org/data/drop
- SciQ
After preprocessing the data from the above listed datasets, we had 408372 examples for training the model and 25k for development and 18k for testing.
## Training procedure
The model is trained (finetuned) for 5 epochs with the hyperparameters listed below:
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-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: cosine
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 5
At the end of 5 epochs, the Evaluation loss was: 1.64 and the training loss was: 0.9671.
### Framework versions
- Transformers 4.23.1
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
} | 855 | null | ---
license: mit
---
### PJablonski style on Stable Diffusion
This is the `<pjablonski-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`:
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
} | 229 | null | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- laion-2b
---
# Model card for vit_base_patch32_clip_224.laion2b_ft_in1k
A Vision Transformer (ViT) image classification model. Pretrained on LAION-2B image-text pairs using OpenCLIP. Fine-tuned on ImageNet-1k in `timm`. See recipes in [Reproducible scaling laws](https://arxiv.org/abs/2212.07143).
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 88.2
- GMACs: 4.4
- Activations (M): 4.2
- Image size: 224 x 224
- **Papers:**
- OpenCLIP: https://github.com/mlfoundations/open_clip
- Reproducible scaling laws for contrastive language-image learning: https://arxiv.org/abs/2212.07143
- LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:**
- LAION-2B
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_base_patch32_clip_224.laion2b_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_base_patch32_clip_224.laion2b_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 50, 768) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
```bibtex
@article{cherti2022reproducible,
title={Reproducible scaling laws for contrastive language-image learning},
author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia},
journal={arXiv preprint arXiv:2212.07143},
year={2022}
}
```
```bibtex
@inproceedings{schuhmann2022laionb,
title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
author={Christoph Schuhmann and
Romain Beaumont and
Richard Vencu and
Cade W Gordon and
Ross Wightman and
Mehdi Cherti and
Theo Coombes and
Aarush Katta and
Clayton Mullis and
Mitchell Wortsman and
Patrick Schramowski and
Srivatsa R Kundurthy and
Katherine Crowson and
Ludwig Schmidt and
Robert Kaczmarczyk and
Jenia Jitsev},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=M3Y74vmsMcY}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-egy | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
}
} | 52 | null | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- laion-2b
---
# Model card for vit_huge_patch14_clip_224.laion2b_ft_in1k
A Vision Transformer (ViT) image classification model. Pretrained on LAION-2B image-text pairs using OpenCLIP. Fine-tuned on ImageNet-1k in `timm`. See recipes in [Reproducible scaling laws](https://arxiv.org/abs/2212.07143).
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 632.0
- GMACs: 162.0
- Activations (M): 95.1
- Image size: 224 x 224
- **Papers:**
- OpenCLIP: https://github.com/mlfoundations/open_clip
- Reproducible scaling laws for contrastive language-image learning: https://arxiv.org/abs/2212.07143
- LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:**
- LAION-2B
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_huge_patch14_clip_224.laion2b_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_huge_patch14_clip_224.laion2b_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 257, 1280) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
```bibtex
@article{cherti2022reproducible,
title={Reproducible scaling laws for contrastive language-image learning},
author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia},
journal={arXiv preprint arXiv:2212.07143},
year={2022}
}
```
```bibtex
@inproceedings{schuhmann2022laionb,
title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
author={Christoph Schuhmann and
Romain Beaumont and
Richard Vencu and
Cade W Gordon and
Ross Wightman and
Mehdi Cherti and
Theo Coombes and
Aarush Katta and
Clayton Mullis and
Mitchell Wortsman and
Patrick Schramowski and
Srivatsa R Kundurthy and
Katherine Crowson and
Ludwig Schmidt and
Robert Kaczmarczyk and
Jenia Jitsev},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=M3Y74vmsMcY}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 2,967 | null | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-12k
- laion-2b
---
# Model card for vit_large_patch14_clip_224.laion2b_ft_in12k
A Vision Transformer (ViT) image classification model. Pretrained on LAION-2B image-text pairs using OpenCLIP. Fine-tuned on ImageNet-12k in `timm`. See recipes in [Reproducible scaling laws](https://arxiv.org/abs/2212.07143).
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 315.3
- GMACs: 77.8
- Activations (M): 57.1
- Image size: 224 x 224
- **Papers:**
- OpenCLIP: https://github.com/mlfoundations/open_clip
- Reproducible scaling laws for contrastive language-image learning: https://arxiv.org/abs/2212.07143
- LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Dataset:** ImageNet-12k
- **Pretrain Dataset:**
- LAION-2B
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_large_patch14_clip_224.laion2b_ft_in12k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_large_patch14_clip_224.laion2b_ft_in12k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 257, 1024) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
```bibtex
@article{cherti2022reproducible,
title={Reproducible scaling laws for contrastive language-image learning},
author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia},
journal={arXiv preprint arXiv:2212.07143},
year={2022}
}
```
```bibtex
@inproceedings{schuhmann2022laionb,
title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
author={Christoph Schuhmann and
Romain Beaumont and
Richard Vencu and
Cade W Gordon and
Ross Wightman and
Mehdi Cherti and
Theo Coombes and
Aarush Katta and
Clayton Mullis and
Mitchell Wortsman and
Patrick Schramowski and
Srivatsa R Kundurthy and
Katherine Crowson and
Ludwig Schmidt and
Robert Kaczmarczyk and
Jenia Jitsev},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=M3Y74vmsMcY}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
CAUKiel/JavaBERT | [
"pytorch",
"safetensors",
"bert",
"fill-mask",
"code",
"arxiv:2110.10404",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 388 | 2022-11-01T23:03:50Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
model-index:
- name: convnext-tiny-224-finetuned-brs2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7924528301886793
- name: F1
type: f1
value: 0.7555555555555556
---
<!-- 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. -->
# convnext-tiny-224-finetuned-brs2
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2502
- Accuracy: 0.7925
- F1: 0.7556
- Precision (ppv): 0.8095
- Recall (sensitivity): 0.7083
- Specificity: 0.8621
- Npv: 0.7812
- Auc: 0.7852
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision (ppv) | Recall (sensitivity) | Specificity | Npv | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:|
| 0.6884 | 1.89 | 100 | 0.6907 | 0.5472 | 0.4286 | 0.5 | 0.375 | 0.6897 | 0.5714 | 0.5323 |
| 0.5868 | 3.77 | 200 | 0.6604 | 0.6415 | 0.4242 | 0.7778 | 0.2917 | 0.9310 | 0.6136 | 0.6114 |
| 0.4759 | 5.66 | 300 | 0.6273 | 0.6604 | 0.5 | 0.75 | 0.375 | 0.8966 | 0.6341 | 0.6358 |
| 0.3599 | 7.55 | 400 | 0.6520 | 0.6604 | 0.5 | 0.75 | 0.375 | 0.8966 | 0.6341 | 0.6358 |
| 0.3248 | 9.43 | 500 | 0.9115 | 0.6415 | 0.4571 | 0.7273 | 0.3333 | 0.8966 | 0.6190 | 0.6149 |
| 0.3117 | 11.32 | 600 | 0.8608 | 0.6604 | 0.5263 | 0.7143 | 0.4167 | 0.8621 | 0.6410 | 0.6394 |
| 0.4208 | 13.21 | 700 | 0.8774 | 0.6792 | 0.5641 | 0.7333 | 0.4583 | 0.8621 | 0.6579 | 0.6602 |
| 0.5267 | 15.09 | 800 | 1.0131 | 0.6792 | 0.5405 | 0.7692 | 0.4167 | 0.8966 | 0.65 | 0.6566 |
| 0.234 | 16.98 | 900 | 1.1498 | 0.6981 | 0.5556 | 0.8333 | 0.4167 | 0.9310 | 0.6585 | 0.6739 |
| 0.7581 | 18.87 | 1000 | 1.0952 | 0.7170 | 0.6154 | 0.8 | 0.5 | 0.8966 | 0.6842 | 0.6983 |
| 0.1689 | 20.75 | 1100 | 1.1653 | 0.6981 | 0.5789 | 0.7857 | 0.4583 | 0.8966 | 0.6667 | 0.6774 |
| 0.0765 | 22.64 | 1200 | 1.1245 | 0.7170 | 0.6667 | 0.7143 | 0.625 | 0.7931 | 0.7188 | 0.7091 |
| 0.6287 | 24.53 | 1300 | 1.2222 | 0.6981 | 0.6 | 0.75 | 0.5 | 0.8621 | 0.6757 | 0.6810 |
| 0.0527 | 26.42 | 1400 | 1.2350 | 0.7358 | 0.6818 | 0.75 | 0.625 | 0.8276 | 0.7273 | 0.7263 |
| 0.3622 | 28.3 | 1500 | 1.1022 | 0.7547 | 0.6667 | 0.8667 | 0.5417 | 0.9310 | 0.7105 | 0.7364 |
| 0.3227 | 30.19 | 1600 | 1.1541 | 0.7170 | 0.6154 | 0.8 | 0.5 | 0.8966 | 0.6842 | 0.6983 |
| 0.3849 | 32.08 | 1700 | 1.2818 | 0.7170 | 0.6154 | 0.8 | 0.5 | 0.8966 | 0.6842 | 0.6983 |
| 0.4528 | 33.96 | 1800 | 1.3213 | 0.6981 | 0.5789 | 0.7857 | 0.4583 | 0.8966 | 0.6667 | 0.6774 |
| 0.1824 | 35.85 | 1900 | 1.3171 | 0.7170 | 0.6512 | 0.7368 | 0.5833 | 0.8276 | 0.7059 | 0.7055 |
| 0.0367 | 37.74 | 2000 | 1.4484 | 0.7170 | 0.6154 | 0.8 | 0.5 | 0.8966 | 0.6842 | 0.6983 |
| 0.07 | 39.62 | 2100 | 1.3521 | 0.7547 | 0.6977 | 0.7895 | 0.625 | 0.8621 | 0.7353 | 0.7435 |
| 0.0696 | 41.51 | 2200 | 1.2636 | 0.7358 | 0.65 | 0.8125 | 0.5417 | 0.8966 | 0.7027 | 0.7191 |
| 0.1554 | 43.4 | 2300 | 1.2225 | 0.7358 | 0.6667 | 0.7778 | 0.5833 | 0.8621 | 0.7143 | 0.7227 |
| 0.2346 | 45.28 | 2400 | 1.2627 | 0.7547 | 0.6829 | 0.8235 | 0.5833 | 0.8966 | 0.7222 | 0.7399 |
| 0.097 | 47.17 | 2500 | 1.4892 | 0.7170 | 0.6667 | 0.7143 | 0.625 | 0.7931 | 0.7188 | 0.7091 |
| 0.2494 | 49.06 | 2600 | 1.5282 | 0.7170 | 0.6512 | 0.7368 | 0.5833 | 0.8276 | 0.7059 | 0.7055 |
| 0.0734 | 50.94 | 2700 | 1.3989 | 0.7170 | 0.6341 | 0.7647 | 0.5417 | 0.8621 | 0.6944 | 0.7019 |
| 0.1077 | 52.83 | 2800 | 1.5155 | 0.6792 | 0.5641 | 0.7333 | 0.4583 | 0.8621 | 0.6579 | 0.6602 |
| 0.2456 | 54.72 | 2900 | 1.4400 | 0.7170 | 0.6512 | 0.7368 | 0.5833 | 0.8276 | 0.7059 | 0.7055 |
| 0.0823 | 56.6 | 3000 | 1.4511 | 0.7358 | 0.65 | 0.8125 | 0.5417 | 0.8966 | 0.7027 | 0.7191 |
| 0.0471 | 58.49 | 3100 | 1.5114 | 0.7547 | 0.6829 | 0.8235 | 0.5833 | 0.8966 | 0.7222 | 0.7399 |
| 0.0144 | 60.38 | 3200 | 1.4412 | 0.7925 | 0.7317 | 0.8824 | 0.625 | 0.9310 | 0.75 | 0.7780 |
| 0.1235 | 62.26 | 3300 | 1.2029 | 0.7547 | 0.6977 | 0.7895 | 0.625 | 0.8621 | 0.7353 | 0.7435 |
| 0.0121 | 64.15 | 3400 | 1.4925 | 0.7358 | 0.6667 | 0.7778 | 0.5833 | 0.8621 | 0.7143 | 0.7227 |
| 0.2126 | 66.04 | 3500 | 1.3614 | 0.7547 | 0.6667 | 0.8667 | 0.5417 | 0.9310 | 0.7105 | 0.7364 |
| 0.0496 | 67.92 | 3600 | 1.2960 | 0.7736 | 0.7143 | 0.8333 | 0.625 | 0.8966 | 0.7429 | 0.7608 |
| 0.1145 | 69.81 | 3700 | 1.3763 | 0.7547 | 0.6829 | 0.8235 | 0.5833 | 0.8966 | 0.7222 | 0.7399 |
| 0.1272 | 71.7 | 3800 | 1.6328 | 0.7170 | 0.5946 | 0.8462 | 0.4583 | 0.9310 | 0.675 | 0.6947 |
| 0.0007 | 73.58 | 3900 | 1.5622 | 0.7547 | 0.6977 | 0.7895 | 0.625 | 0.8621 | 0.7353 | 0.7435 |
| 0.0101 | 75.47 | 4000 | 1.1811 | 0.7925 | 0.7442 | 0.8421 | 0.6667 | 0.8966 | 0.7647 | 0.7816 |
| 0.0002 | 77.36 | 4100 | 1.8533 | 0.6981 | 0.5789 | 0.7857 | 0.4583 | 0.8966 | 0.6667 | 0.6774 |
| 0.0423 | 79.25 | 4200 | 1.2510 | 0.7547 | 0.6977 | 0.7895 | 0.625 | 0.8621 | 0.7353 | 0.7435 |
| 0.0036 | 81.13 | 4300 | 1.3443 | 0.7547 | 0.6829 | 0.8235 | 0.5833 | 0.8966 | 0.7222 | 0.7399 |
| 0.0432 | 83.02 | 4400 | 1.2864 | 0.7736 | 0.7273 | 0.8 | 0.6667 | 0.8621 | 0.7576 | 0.7644 |
| 0.0021 | 84.91 | 4500 | 0.8999 | 0.7925 | 0.7755 | 0.76 | 0.7917 | 0.7931 | 0.8214 | 0.7924 |
| 0.0002 | 86.79 | 4600 | 1.3634 | 0.7925 | 0.7442 | 0.8421 | 0.6667 | 0.8966 | 0.7647 | 0.7816 |
| 0.0044 | 88.68 | 4700 | 1.7830 | 0.7358 | 0.65 | 0.8125 | 0.5417 | 0.8966 | 0.7027 | 0.7191 |
| 0.0003 | 90.57 | 4800 | 1.2640 | 0.7736 | 0.7273 | 0.8 | 0.6667 | 0.8621 | 0.7576 | 0.7644 |
| 0.0253 | 92.45 | 4900 | 1.2649 | 0.7925 | 0.7442 | 0.8421 | 0.6667 | 0.8966 | 0.7647 | 0.7816 |
| 0.0278 | 94.34 | 5000 | 1.7485 | 0.7170 | 0.6512 | 0.7368 | 0.5833 | 0.8276 | 0.7059 | 0.7055 |
| 0.1608 | 96.23 | 5100 | 1.2641 | 0.8113 | 0.7727 | 0.85 | 0.7083 | 0.8966 | 0.7879 | 0.8024 |
| 0.0017 | 98.11 | 5200 | 1.6380 | 0.7170 | 0.6667 | 0.7143 | 0.625 | 0.7931 | 0.7188 | 0.7091 |
| 0.001 | 100.0 | 5300 | 1.2502 | 0.7925 | 0.7556 | 0.8095 | 0.7083 | 0.8621 | 0.7812 | 0.7852 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
CLAck/indo-mixed | [
"pytorch",
"marian",
"text2text-generation",
"en",
"id",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 15 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_polarity
metrics:
- accuracy
model-index:
- name: amazonPolarity_XLNET_5E
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_polarity
type: amazon_polarity
config: amazon_polarity
split: train
args: amazon_polarity
metrics:
- name: Accuracy
type: accuracy
value: 0.9266666666666666
---
<!-- 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. -->
# amazonPolarity_XLNET_5E
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the amazon_polarity dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4490
- Accuracy: 0.9267
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6238 | 0.01 | 50 | 0.3703 | 0.86 |
| 0.3149 | 0.03 | 100 | 0.3715 | 0.9 |
| 0.3849 | 0.04 | 150 | 0.4125 | 0.8867 |
| 0.4051 | 0.05 | 200 | 0.4958 | 0.8933 |
| 0.3345 | 0.07 | 250 | 0.4258 | 0.9067 |
| 0.439 | 0.08 | 300 | 0.2650 | 0.9067 |
| 0.2248 | 0.09 | 350 | 0.3314 | 0.9267 |
| 0.2849 | 0.11 | 400 | 0.3097 | 0.8933 |
| 0.3468 | 0.12 | 450 | 0.3060 | 0.9067 |
| 0.3216 | 0.13 | 500 | 0.3826 | 0.9067 |
| 0.3462 | 0.15 | 550 | 0.2207 | 0.94 |
| 0.3632 | 0.16 | 600 | 0.1864 | 0.94 |
| 0.2483 | 0.17 | 650 | 0.3069 | 0.9267 |
| 0.3709 | 0.19 | 700 | 0.2859 | 0.9333 |
| 0.2953 | 0.2 | 750 | 0.3010 | 0.9333 |
| 0.3222 | 0.21 | 800 | 0.2668 | 0.9133 |
| 0.3142 | 0.23 | 850 | 0.3545 | 0.8667 |
| 0.2637 | 0.24 | 900 | 0.1922 | 0.9467 |
| 0.3929 | 0.25 | 950 | 0.2712 | 0.92 |
| 0.2918 | 0.27 | 1000 | 0.2516 | 0.9333 |
| 0.2269 | 0.28 | 1050 | 0.4227 | 0.8933 |
| 0.239 | 0.29 | 1100 | 0.3639 | 0.9133 |
| 0.2439 | 0.31 | 1150 | 0.3430 | 0.9133 |
| 0.2417 | 0.32 | 1200 | 0.2920 | 0.94 |
| 0.3223 | 0.33 | 1250 | 0.3426 | 0.9067 |
| 0.2775 | 0.35 | 1300 | 0.3752 | 0.8867 |
| 0.2733 | 0.36 | 1350 | 0.3015 | 0.9333 |
| 0.3737 | 0.37 | 1400 | 0.2875 | 0.9267 |
| 0.2907 | 0.39 | 1450 | 0.4926 | 0.8933 |
| 0.316 | 0.4 | 1500 | 0.2948 | 0.9333 |
| 0.2472 | 0.41 | 1550 | 0.4003 | 0.8933 |
| 0.2607 | 0.43 | 1600 | 0.3608 | 0.92 |
| 0.2848 | 0.44 | 1650 | 0.3332 | 0.9133 |
| 0.2708 | 0.45 | 1700 | 0.3424 | 0.92 |
| 0.3721 | 0.47 | 1750 | 0.2384 | 0.9267 |
| 0.2925 | 0.48 | 1800 | 0.4472 | 0.88 |
| 0.3619 | 0.49 | 1850 | 0.3824 | 0.9 |
| 0.1994 | 0.51 | 1900 | 0.4160 | 0.9133 |
| 0.3586 | 0.52 | 1950 | 0.3198 | 0.8867 |
| 0.2455 | 0.53 | 2000 | 0.3119 | 0.92 |
| 0.2683 | 0.55 | 2050 | 0.4262 | 0.8867 |
| 0.2983 | 0.56 | 2100 | 0.3552 | 0.9067 |
| 0.2973 | 0.57 | 2150 | 0.2966 | 0.8933 |
| 0.2299 | 0.59 | 2200 | 0.2972 | 0.92 |
| 0.295 | 0.6 | 2250 | 0.3122 | 0.9067 |
| 0.2716 | 0.61 | 2300 | 0.2556 | 0.9267 |
| 0.2842 | 0.63 | 2350 | 0.3317 | 0.92 |
| 0.2723 | 0.64 | 2400 | 0.4409 | 0.8933 |
| 0.2492 | 0.65 | 2450 | 0.3871 | 0.88 |
| 0.2297 | 0.67 | 2500 | 0.3526 | 0.9133 |
| 0.2125 | 0.68 | 2550 | 0.4597 | 0.9067 |
| 0.3003 | 0.69 | 2600 | 0.3374 | 0.8933 |
| 0.2622 | 0.71 | 2650 | 0.3492 | 0.9267 |
| 0.2436 | 0.72 | 2700 | 0.3438 | 0.9267 |
| 0.2599 | 0.73 | 2750 | 0.3725 | 0.9133 |
| 0.2759 | 0.75 | 2800 | 0.3260 | 0.9333 |
| 0.1841 | 0.76 | 2850 | 0.4218 | 0.9067 |
| 0.252 | 0.77 | 2900 | 0.2730 | 0.92 |
| 0.248 | 0.79 | 2950 | 0.3628 | 0.92 |
| 0.2356 | 0.8 | 3000 | 0.4012 | 0.9067 |
| 0.191 | 0.81 | 3050 | 0.3500 | 0.9267 |
| 0.2351 | 0.83 | 3100 | 0.4038 | 0.9133 |
| 0.2758 | 0.84 | 3150 | 0.3361 | 0.9067 |
| 0.2952 | 0.85 | 3200 | 0.2301 | 0.9267 |
| 0.2137 | 0.87 | 3250 | 0.3837 | 0.9133 |
| 0.2386 | 0.88 | 3300 | 0.2739 | 0.94 |
| 0.2786 | 0.89 | 3350 | 0.2820 | 0.9333 |
| 0.2284 | 0.91 | 3400 | 0.2557 | 0.9333 |
| 0.2546 | 0.92 | 3450 | 0.2744 | 0.9267 |
| 0.2514 | 0.93 | 3500 | 0.2908 | 0.94 |
| 0.3052 | 0.95 | 3550 | 0.2362 | 0.9333 |
| 0.2366 | 0.96 | 3600 | 0.3047 | 0.9333 |
| 0.2147 | 0.97 | 3650 | 0.3375 | 0.9333 |
| 0.3347 | 0.99 | 3700 | 0.2669 | 0.9267 |
| 0.3076 | 1.0 | 3750 | 0.2453 | 0.94 |
| 0.1685 | 1.01 | 3800 | 0.4117 | 0.9133 |
| 0.1954 | 1.03 | 3850 | 0.3074 | 0.9333 |
| 0.2512 | 1.04 | 3900 | 0.3942 | 0.9133 |
| 0.1365 | 1.05 | 3950 | 0.3211 | 0.92 |
| 0.1985 | 1.07 | 4000 | 0.4188 | 0.9133 |
| 0.1585 | 1.08 | 4050 | 0.4177 | 0.9133 |
| 0.1798 | 1.09 | 4100 | 0.3298 | 0.9333 |
| 0.1458 | 1.11 | 4150 | 0.5283 | 0.9 |
| 0.1831 | 1.12 | 4200 | 0.3884 | 0.92 |
| 0.1452 | 1.13 | 4250 | 0.4130 | 0.9133 |
| 0.1679 | 1.15 | 4300 | 0.3678 | 0.9267 |
| 0.1688 | 1.16 | 4350 | 0.3268 | 0.9333 |
| 0.1175 | 1.17 | 4400 | 0.4722 | 0.92 |
| 0.1661 | 1.19 | 4450 | 0.3899 | 0.9133 |
| 0.1688 | 1.2 | 4500 | 0.4050 | 0.9133 |
| 0.228 | 1.21 | 4550 | 0.4608 | 0.9 |
| 0.1946 | 1.23 | 4600 | 0.5080 | 0.9 |
| 0.1849 | 1.24 | 4650 | 0.4340 | 0.9067 |
| 0.1365 | 1.25 | 4700 | 0.4592 | 0.9133 |
| 0.2432 | 1.27 | 4750 | 0.3683 | 0.92 |
| 0.1679 | 1.28 | 4800 | 0.4604 | 0.9 |
| 0.2107 | 1.29 | 4850 | 0.3952 | 0.9 |
| 0.1499 | 1.31 | 4900 | 0.4275 | 0.92 |
| 0.1504 | 1.32 | 4950 | 0.3370 | 0.9333 |
| 0.1013 | 1.33 | 5000 | 0.3723 | 0.92 |
| 0.1303 | 1.35 | 5050 | 0.2925 | 0.9333 |
| 0.1205 | 1.36 | 5100 | 0.3452 | 0.9267 |
| 0.1427 | 1.37 | 5150 | 0.3080 | 0.94 |
| 0.1518 | 1.39 | 5200 | 0.3190 | 0.94 |
| 0.1885 | 1.4 | 5250 | 0.2726 | 0.9467 |
| 0.1264 | 1.41 | 5300 | 0.3466 | 0.9333 |
| 0.1939 | 1.43 | 5350 | 0.3957 | 0.9133 |
| 0.1939 | 1.44 | 5400 | 0.4007 | 0.9 |
| 0.1239 | 1.45 | 5450 | 0.2924 | 0.9333 |
| 0.1588 | 1.47 | 5500 | 0.2687 | 0.9333 |
| 0.1516 | 1.48 | 5550 | 0.3668 | 0.92 |
| 0.1623 | 1.49 | 5600 | 0.3141 | 0.94 |
| 0.2632 | 1.51 | 5650 | 0.2714 | 0.9333 |
| 0.1674 | 1.52 | 5700 | 0.3188 | 0.94 |
| 0.1854 | 1.53 | 5750 | 0.2818 | 0.9267 |
| 0.1282 | 1.55 | 5800 | 0.2918 | 0.9333 |
| 0.228 | 1.56 | 5850 | 0.2802 | 0.9133 |
| 0.2349 | 1.57 | 5900 | 0.1803 | 0.9467 |
| 0.1608 | 1.59 | 5950 | 0.3112 | 0.92 |
| 0.1493 | 1.6 | 6000 | 0.3018 | 0.9267 |
| 0.2182 | 1.61 | 6050 | 0.3419 | 0.9333 |
| 0.2408 | 1.63 | 6100 | 0.2887 | 0.9267 |
| 0.1872 | 1.64 | 6150 | 0.2408 | 0.9267 |
| 0.1246 | 1.65 | 6200 | 0.3752 | 0.9 |
| 0.2098 | 1.67 | 6250 | 0.2622 | 0.9333 |
| 0.1916 | 1.68 | 6300 | 0.2245 | 0.9467 |
| 0.2069 | 1.69 | 6350 | 0.2151 | 0.9467 |
| 0.1446 | 1.71 | 6400 | 0.2186 | 0.9533 |
| 0.1528 | 1.72 | 6450 | 0.1863 | 0.9533 |
| 0.1352 | 1.73 | 6500 | 0.2660 | 0.9467 |
| 0.2398 | 1.75 | 6550 | 0.1912 | 0.9533 |
| 0.1485 | 1.76 | 6600 | 0.2492 | 0.9467 |
| 0.2006 | 1.77 | 6650 | 0.2495 | 0.9267 |
| 0.2036 | 1.79 | 6700 | 0.3885 | 0.9067 |
| 0.1725 | 1.8 | 6750 | 0.2359 | 0.9533 |
| 0.1864 | 1.81 | 6800 | 0.2271 | 0.9533 |
| 0.1465 | 1.83 | 6850 | 0.2669 | 0.9333 |
| 0.197 | 1.84 | 6900 | 0.2290 | 0.96 |
| 0.1382 | 1.85 | 6950 | 0.2322 | 0.9467 |
| 0.1206 | 1.87 | 7000 | 0.3117 | 0.9333 |
| 0.157 | 1.88 | 7050 | 0.2163 | 0.9533 |
| 0.1686 | 1.89 | 7100 | 0.2239 | 0.9533 |
| 0.1953 | 1.91 | 7150 | 0.3064 | 0.9333 |
| 0.1638 | 1.92 | 7200 | 0.2821 | 0.9533 |
| 0.1605 | 1.93 | 7250 | 0.2413 | 0.9467 |
| 0.1736 | 1.95 | 7300 | 0.2430 | 0.94 |
| 0.2372 | 1.96 | 7350 | 0.2306 | 0.94 |
| 0.1549 | 1.97 | 7400 | 0.2730 | 0.94 |
| 0.1824 | 1.99 | 7450 | 0.3443 | 0.94 |
| 0.2263 | 2.0 | 7500 | 0.2695 | 0.9267 |
| 0.088 | 2.01 | 7550 | 0.2305 | 0.96 |
| 0.0376 | 2.03 | 7600 | 0.3380 | 0.94 |
| 0.072 | 2.04 | 7650 | 0.3349 | 0.9467 |
| 0.0491 | 2.05 | 7700 | 0.3397 | 0.94 |
| 0.0509 | 2.07 | 7750 | 0.3496 | 0.9467 |
| 0.1033 | 2.08 | 7800 | 0.3364 | 0.94 |
| 0.0549 | 2.09 | 7850 | 0.3520 | 0.94 |
| 0.0627 | 2.11 | 7900 | 0.4510 | 0.9267 |
| 0.0283 | 2.12 | 7950 | 0.3733 | 0.94 |
| 0.1215 | 2.13 | 8000 | 0.3892 | 0.9267 |
| 0.0856 | 2.15 | 8050 | 0.3114 | 0.9533 |
| 0.0945 | 2.16 | 8100 | 0.3626 | 0.9333 |
| 0.0901 | 2.17 | 8150 | 0.3116 | 0.94 |
| 0.0688 | 2.19 | 8200 | 0.3515 | 0.9267 |
| 0.1286 | 2.2 | 8250 | 0.3255 | 0.9333 |
| 0.1043 | 2.21 | 8300 | 0.4395 | 0.9133 |
| 0.1199 | 2.23 | 8350 | 0.3307 | 0.94 |
| 0.0608 | 2.24 | 8400 | 0.2992 | 0.9533 |
| 0.0827 | 2.25 | 8450 | 0.3500 | 0.94 |
| 0.047 | 2.27 | 8500 | 0.3982 | 0.94 |
| 0.1154 | 2.28 | 8550 | 0.3851 | 0.94 |
| 0.1158 | 2.29 | 8600 | 0.3820 | 0.9133 |
| 0.1053 | 2.31 | 8650 | 0.4414 | 0.92 |
| 0.1336 | 2.32 | 8700 | 0.3680 | 0.92 |
| 0.0853 | 2.33 | 8750 | 0.3732 | 0.9333 |
| 0.0496 | 2.35 | 8800 | 0.3450 | 0.94 |
| 0.0552 | 2.36 | 8850 | 0.4310 | 0.9267 |
| 0.1054 | 2.37 | 8900 | 0.4174 | 0.92 |
| 0.0951 | 2.39 | 8950 | 0.3815 | 0.9333 |
| 0.1235 | 2.4 | 9000 | 0.4119 | 0.9267 |
| 0.1094 | 2.41 | 9050 | 0.4282 | 0.9133 |
| 0.0897 | 2.43 | 9100 | 0.4766 | 0.9133 |
| 0.0925 | 2.44 | 9150 | 0.3303 | 0.94 |
| 0.1487 | 2.45 | 9200 | 0.2948 | 0.94 |
| 0.0963 | 2.47 | 9250 | 0.2911 | 0.94 |
| 0.0836 | 2.48 | 9300 | 0.3379 | 0.94 |
| 0.1594 | 2.49 | 9350 | 0.3841 | 0.9267 |
| 0.0846 | 2.51 | 9400 | 0.4128 | 0.9267 |
| 0.0984 | 2.52 | 9450 | 0.4131 | 0.9333 |
| 0.1042 | 2.53 | 9500 | 0.4048 | 0.9267 |
| 0.0633 | 2.55 | 9550 | 0.3776 | 0.94 |
| 0.1266 | 2.56 | 9600 | 0.3247 | 0.9333 |
| 0.1084 | 2.57 | 9650 | 0.3174 | 0.9467 |
| 0.0714 | 2.59 | 9700 | 0.3597 | 0.94 |
| 0.0826 | 2.6 | 9750 | 0.3261 | 0.9467 |
| 0.1527 | 2.61 | 9800 | 0.2531 | 0.9533 |
| 0.0506 | 2.63 | 9850 | 0.2994 | 0.9533 |
| 0.1043 | 2.64 | 9900 | 0.3345 | 0.9467 |
| 0.0229 | 2.65 | 9950 | 0.4318 | 0.9333 |
| 0.1247 | 2.67 | 10000 | 0.2951 | 0.9533 |
| 0.1285 | 2.68 | 10050 | 0.3036 | 0.9533 |
| 0.081 | 2.69 | 10100 | 0.3541 | 0.94 |
| 0.0829 | 2.71 | 10150 | 0.3757 | 0.9467 |
| 0.0702 | 2.72 | 10200 | 0.3307 | 0.9533 |
| 0.07 | 2.73 | 10250 | 0.3638 | 0.94 |
| 0.1563 | 2.75 | 10300 | 0.3283 | 0.94 |
| 0.1223 | 2.76 | 10350 | 0.3441 | 0.92 |
| 0.0954 | 2.77 | 10400 | 0.3049 | 0.94 |
| 0.0438 | 2.79 | 10450 | 0.3675 | 0.9467 |
| 0.0796 | 2.8 | 10500 | 0.3364 | 0.94 |
| 0.0803 | 2.81 | 10550 | 0.2970 | 0.94 |
| 0.0324 | 2.83 | 10600 | 0.3941 | 0.9267 |
| 0.083 | 2.84 | 10650 | 0.3439 | 0.94 |
| 0.1263 | 2.85 | 10700 | 0.3759 | 0.9267 |
| 0.1044 | 2.87 | 10750 | 1.0700 | 0.58 |
| 0.1182 | 2.88 | 10800 | 0.4409 | 0.9333 |
| 0.126 | 2.89 | 10850 | 0.6467 | 0.5933 |
| 0.094 | 2.91 | 10900 | 0.3741 | 0.9333 |
| 0.1405 | 2.92 | 10950 | 0.3458 | 0.9267 |
| 0.1024 | 2.93 | 11000 | 0.2946 | 0.9333 |
| 0.0812 | 2.95 | 11050 | 0.2850 | 0.9333 |
| 0.1132 | 2.96 | 11100 | 0.3093 | 0.9267 |
| 0.0775 | 2.97 | 11150 | 0.3938 | 0.9067 |
| 0.1179 | 2.99 | 11200 | 0.3528 | 0.9267 |
| 0.1413 | 3.0 | 11250 | 0.2984 | 0.9333 |
| 0.0528 | 3.01 | 11300 | 0.3387 | 0.9333 |
| 0.0214 | 3.03 | 11350 | 0.4108 | 0.92 |
| 0.0408 | 3.04 | 11400 | 0.4174 | 0.9267 |
| 0.0808 | 3.05 | 11450 | 0.4283 | 0.9267 |
| 0.0535 | 3.07 | 11500 | 0.3719 | 0.9333 |
| 0.0344 | 3.08 | 11550 | 0.4382 | 0.9333 |
| 0.0364 | 3.09 | 11600 | 0.4195 | 0.9333 |
| 0.0524 | 3.11 | 11650 | 0.4607 | 0.92 |
| 0.0682 | 3.12 | 11700 | 0.4503 | 0.92 |
| 0.0554 | 3.13 | 11750 | 0.4563 | 0.92 |
| 0.0401 | 3.15 | 11800 | 0.4668 | 0.9133 |
| 0.0782 | 3.16 | 11850 | 0.4468 | 0.9133 |
| 0.0605 | 3.17 | 11900 | 0.4239 | 0.92 |
| 0.0599 | 3.19 | 11950 | 0.4019 | 0.92 |
| 0.0364 | 3.2 | 12000 | 0.3988 | 0.9267 |
| 0.0357 | 3.21 | 12050 | 0.4168 | 0.9267 |
| 0.072 | 3.23 | 12100 | 0.3889 | 0.9333 |
| 0.0931 | 3.24 | 12150 | 0.3368 | 0.9333 |
| 0.0724 | 3.25 | 12200 | 0.3209 | 0.9333 |
| 0.0653 | 3.27 | 12250 | 0.3615 | 0.9333 |
| 0.0173 | 3.28 | 12300 | 0.3946 | 0.9333 |
| 0.0537 | 3.29 | 12350 | 0.3876 | 0.9333 |
| 0.0373 | 3.31 | 12400 | 0.4079 | 0.9267 |
| 0.0322 | 3.32 | 12450 | 0.3553 | 0.94 |
| 0.0585 | 3.33 | 12500 | 0.4276 | 0.92 |
| 0.0315 | 3.35 | 12550 | 0.4092 | 0.9267 |
| 0.0317 | 3.36 | 12600 | 0.4107 | 0.9267 |
| 0.082 | 3.37 | 12650 | 0.4170 | 0.9267 |
| 0.1101 | 3.39 | 12700 | 0.3801 | 0.9333 |
| 0.0392 | 3.4 | 12750 | 0.3802 | 0.9333 |
| 0.0382 | 3.41 | 12800 | 0.4194 | 0.9267 |
| 0.048 | 3.43 | 12850 | 0.3794 | 0.9333 |
| 0.0896 | 3.44 | 12900 | 0.3961 | 0.9267 |
| 0.0966 | 3.45 | 12950 | 0.3982 | 0.92 |
| 0.0165 | 3.47 | 13000 | 0.3819 | 0.92 |
| 0.0701 | 3.48 | 13050 | 0.3440 | 0.94 |
| 0.0104 | 3.49 | 13100 | 0.4132 | 0.9267 |
| 0.0991 | 3.51 | 13150 | 0.3477 | 0.9333 |
| 0.0554 | 3.52 | 13200 | 0.3255 | 0.94 |
| 0.0476 | 3.53 | 13250 | 0.4343 | 0.92 |
| 0.0213 | 3.55 | 13300 | 0.4601 | 0.92 |
| 0.0465 | 3.56 | 13350 | 0.4141 | 0.9267 |
| 0.1246 | 3.57 | 13400 | 0.3473 | 0.94 |
| 0.1112 | 3.59 | 13450 | 0.3679 | 0.92 |
| 0.0323 | 3.6 | 13500 | 0.3508 | 0.9267 |
| 0.0423 | 3.61 | 13550 | 0.3475 | 0.94 |
| 0.0498 | 3.63 | 13600 | 0.4095 | 0.92 |
| 0.0531 | 3.64 | 13650 | 0.3544 | 0.9333 |
| 0.0365 | 3.65 | 13700 | 0.4403 | 0.9133 |
| 0.058 | 3.67 | 13750 | 0.4284 | 0.9133 |
| 0.0191 | 3.68 | 13800 | 0.4466 | 0.92 |
| 0.0838 | 3.69 | 13850 | 0.5128 | 0.9067 |
| 0.1561 | 3.71 | 13900 | 0.3588 | 0.9267 |
| 0.0464 | 3.72 | 13950 | 0.3867 | 0.92 |
| 0.037 | 3.73 | 14000 | 0.3961 | 0.92 |
| 0.0288 | 3.75 | 14050 | 0.4274 | 0.92 |
| 0.0928 | 3.76 | 14100 | 0.3524 | 0.94 |
| 0.0696 | 3.77 | 14150 | 0.3555 | 0.9333 |
| 0.0318 | 3.79 | 14200 | 0.3457 | 0.9467 |
| 0.0417 | 3.8 | 14250 | 0.3412 | 0.94 |
| 0.0283 | 3.81 | 14300 | 0.3845 | 0.9333 |
| 0.058 | 3.83 | 14350 | 0.3765 | 0.9333 |
| 0.0589 | 3.84 | 14400 | 0.4085 | 0.9267 |
| 0.0432 | 3.85 | 14450 | 0.4103 | 0.9267 |
| 0.0365 | 3.87 | 14500 | 0.4000 | 0.9267 |
| 0.0858 | 3.88 | 14550 | 0.3905 | 0.9267 |
| 0.0494 | 3.89 | 14600 | 0.3739 | 0.9267 |
| 0.0503 | 3.91 | 14650 | 0.3203 | 0.94 |
| 0.0349 | 3.92 | 14700 | 0.3268 | 0.9467 |
| 0.0328 | 3.93 | 14750 | 0.3259 | 0.9467 |
| 0.0347 | 3.95 | 14800 | 0.3588 | 0.94 |
| 0.0233 | 3.96 | 14850 | 0.3456 | 0.9467 |
| 0.0602 | 3.97 | 14900 | 0.3819 | 0.94 |
| 0.0766 | 3.99 | 14950 | 0.3813 | 0.9333 |
| 0.0562 | 4.0 | 15000 | 0.3669 | 0.9333 |
| 0.0163 | 4.01 | 15050 | 0.4176 | 0.92 |
| 0.007 | 4.03 | 15100 | 0.3694 | 0.9333 |
| 0.0005 | 4.04 | 15150 | 0.3915 | 0.9333 |
| 0.021 | 4.05 | 15200 | 0.4334 | 0.9333 |
| 0.0823 | 4.07 | 15250 | 0.4155 | 0.9333 |
| 0.0509 | 4.08 | 15300 | 0.4056 | 0.9333 |
| 0.0381 | 4.09 | 15350 | 0.3729 | 0.94 |
| 0.045 | 4.11 | 15400 | 0.3940 | 0.9333 |
| 0.0379 | 4.12 | 15450 | 0.4276 | 0.9267 |
| 0.0661 | 4.13 | 15500 | 0.3797 | 0.94 |
| 0.0522 | 4.15 | 15550 | 0.4029 | 0.9333 |
| 0.0189 | 4.16 | 15600 | 0.4424 | 0.9267 |
| 0.0191 | 4.17 | 15650 | 0.4711 | 0.92 |
| 0.031 | 4.19 | 15700 | 0.4344 | 0.9333 |
| 0.0837 | 4.2 | 15750 | 0.3703 | 0.94 |
| 0.0397 | 4.21 | 15800 | 0.3976 | 0.9333 |
| 0.034 | 4.23 | 15850 | 0.4021 | 0.9333 |
| 0.0199 | 4.24 | 15900 | 0.4015 | 0.9333 |
| 0.0315 | 4.25 | 15950 | 0.3652 | 0.94 |
| 0.076 | 4.27 | 16000 | 0.3421 | 0.94 |
| 0.0478 | 4.28 | 16050 | 0.3122 | 0.9533 |
| 0.0203 | 4.29 | 16100 | 0.3436 | 0.9467 |
| 0.0706 | 4.31 | 16150 | 0.3544 | 0.94 |
| 0.0086 | 4.32 | 16200 | 0.3730 | 0.94 |
| 0.05 | 4.33 | 16250 | 0.3761 | 0.94 |
| 0.048 | 4.35 | 16300 | 0.3583 | 0.94 |
| 0.0715 | 4.36 | 16350 | 0.3459 | 0.94 |
| 0.0316 | 4.37 | 16400 | 0.3355 | 0.94 |
| 0.0356 | 4.39 | 16450 | 0.3278 | 0.9467 |
| 0.0176 | 4.4 | 16500 | 0.3177 | 0.9467 |
| 0.0817 | 4.41 | 16550 | 0.3705 | 0.9333 |
| 0.0414 | 4.43 | 16600 | 0.3919 | 0.9333 |
| 0.0198 | 4.44 | 16650 | 0.3435 | 0.9467 |
| 0.0203 | 4.45 | 16700 | 0.3708 | 0.94 |
| 0.0391 | 4.47 | 16750 | 0.3615 | 0.94 |
| 0.0132 | 4.48 | 16800 | 0.3827 | 0.94 |
| 0.0385 | 4.49 | 16850 | 0.3837 | 0.94 |
| 0.0366 | 4.51 | 16900 | 0.3633 | 0.94 |
| 0.0779 | 4.52 | 16950 | 0.3403 | 0.9467 |
| 0.0168 | 4.53 | 17000 | 0.4592 | 0.92 |
| 0.0517 | 4.55 | 17050 | 0.4063 | 0.9333 |
| 0.0138 | 4.56 | 17100 | 0.4335 | 0.9267 |
| 0.0123 | 4.57 | 17150 | 0.3777 | 0.9333 |
| 0.0324 | 4.59 | 17200 | 0.4657 | 0.92 |
| 0.0202 | 4.6 | 17250 | 0.4791 | 0.92 |
| 0.001 | 4.61 | 17300 | 0.4761 | 0.92 |
| 0.0364 | 4.63 | 17350 | 0.4663 | 0.92 |
| 0.0154 | 4.64 | 17400 | 0.4611 | 0.92 |
| 0.0184 | 4.65 | 17450 | 0.4616 | 0.92 |
| 0.0004 | 4.67 | 17500 | 0.4650 | 0.92 |
| 0.0192 | 4.68 | 17550 | 0.4649 | 0.92 |
| 0.0185 | 4.69 | 17600 | 0.4654 | 0.92 |
| 0.0196 | 4.71 | 17650 | 0.4643 | 0.92 |
| 0.0386 | 4.72 | 17700 | 0.4660 | 0.92 |
| 0.0236 | 4.73 | 17750 | 0.4499 | 0.9267 |
| 0.0383 | 4.75 | 17800 | 0.4479 | 0.9267 |
| 0.0398 | 4.76 | 17850 | 0.4483 | 0.9267 |
| 0.0004 | 4.77 | 17900 | 0.4541 | 0.9267 |
| 0.023 | 4.79 | 17950 | 0.4387 | 0.9267 |
| 0.0361 | 4.8 | 18000 | 0.4409 | 0.9267 |
| 0.0409 | 4.81 | 18050 | 0.4384 | 0.9267 |
| 0.0004 | 4.83 | 18100 | 0.4376 | 0.9267 |
| 0.0171 | 4.84 | 18150 | 0.4421 | 0.9267 |
| 0.0589 | 4.85 | 18200 | 0.4373 | 0.9267 |
| 0.0004 | 4.87 | 18250 | 0.4492 | 0.9267 |
| 0.0142 | 4.88 | 18300 | 0.4585 | 0.9267 |
| 0.0561 | 4.89 | 18350 | 0.4681 | 0.9267 |
| 0.0204 | 4.91 | 18400 | 0.4608 | 0.9267 |
| 0.0248 | 4.92 | 18450 | 0.4641 | 0.9267 |
| 0.0404 | 4.93 | 18500 | 0.4567 | 0.9267 |
| 0.0608 | 4.95 | 18550 | 0.4518 | 0.9267 |
| 0.0412 | 4.96 | 18600 | 0.4510 | 0.9267 |
| 0.0183 | 4.97 | 18650 | 0.4522 | 0.9267 |
| 0.0567 | 4.99 | 18700 | 0.4492 | 0.9267 |
| 0.0173 | 5.0 | 18750 | 0.4490 | 0.9267 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
CLEE/CLEE | []
| null | {
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}
}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-base-finetuned-qg-context-dataset
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-qg-context-dataset
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6222
- Rouge1: 36.2283
- Rouge2: 16.0636
- Rougel: 32.6282
- Rougelsum: 32.6551
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| No log | 1.0 | 73 | 1.8864 | 32.9447 | 13.9495 | 27.5473 | 27.4092 |
| No log | 2.0 | 146 | 1.6866 | 35.1131 | 13.7925 | 30.7017 | 30.5957 |
| No log | 3.0 | 219 | 1.6392 | 30.4209 | 11.2611 | 27.0456 | 27.0847 |
| No log | 4.0 | 292 | 1.6222 | 36.2283 | 16.0636 | 32.6282 | 32.6551 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
CLTL/icf-levels-mbw | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
| text-classification | {
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"RobertaForSequenceClassification"
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},
"translation_en_to_ro": {
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}
}
} | 30 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
model-index:
- name: cvt-21-finetuned-brs2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.660377358490566
- name: F1
type: f1
value: 0.608695652173913
---
<!-- 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. -->
# cvt-21-finetuned-brs2
This model is a fine-tuned version of [microsoft/cvt-21](https://huggingface.co/microsoft/cvt-21) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6947
- Accuracy: 0.6604
- F1: 0.6087
- Precision (ppv): 0.5385
- Recall (sensitivity): 0.7
- Specificity: 0.6364
- Npv: 0.7778
- Auc: 0.6682
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision (ppv) | Recall (sensitivity) | Specificity | Npv | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:|
| 0.8177 | 1.89 | 100 | 0.7113 | 0.5283 | 0.5098 | 0.4194 | 0.65 | 0.4545 | 0.6818 | 0.5523 |
| 0.736 | 3.77 | 200 | 0.7178 | 0.5283 | 0.3902 | 0.3810 | 0.4 | 0.6061 | 0.625 | 0.5030 |
| 0.5978 | 5.66 | 300 | 0.6889 | 0.6038 | 0.5532 | 0.4815 | 0.65 | 0.5758 | 0.7308 | 0.6129 |
| 0.5576 | 7.55 | 400 | 0.7349 | 0.4717 | 0.5484 | 0.4048 | 0.85 | 0.2424 | 0.7273 | 0.5462 |
| 0.5219 | 9.43 | 500 | 0.6522 | 0.6038 | 0.4 | 0.4667 | 0.35 | 0.7576 | 0.6579 | 0.5538 |
| 0.5326 | 11.32 | 600 | 0.6665 | 0.6226 | 0.5238 | 0.5 | 0.55 | 0.6667 | 0.7097 | 0.6083 |
| 0.4381 | 13.21 | 700 | 0.7685 | 0.4717 | 0.5333 | 0.4 | 0.8 | 0.2727 | 0.6923 | 0.5364 |
| 0.5598 | 15.09 | 800 | 0.7212 | 0.5283 | 0.1935 | 0.2727 | 0.15 | 0.7576 | 0.5952 | 0.4538 |
| 0.6887 | 16.98 | 900 | 0.6985 | 0.6604 | 0.64 | 0.5333 | 0.8 | 0.5758 | 0.8261 | 0.6879 |
| 0.7594 | 18.87 | 1000 | 0.7040 | 0.5472 | 0.4286 | 0.4091 | 0.45 | 0.6061 | 0.6452 | 0.5280 |
| 0.2177 | 20.75 | 1100 | 0.8056 | 0.4528 | 0.5397 | 0.3953 | 0.85 | 0.2121 | 0.7 | 0.5311 |
| 0.4893 | 22.64 | 1200 | 0.8821 | 0.3396 | 0.3860 | 0.2973 | 0.55 | 0.2121 | 0.4375 | 0.3811 |
| 0.5994 | 24.53 | 1300 | 0.8059 | 0.5660 | 0.5660 | 0.4545 | 0.75 | 0.4545 | 0.75 | 0.6023 |
| 0.5179 | 26.42 | 1400 | 0.6750 | 0.6038 | 0.4615 | 0.4737 | 0.45 | 0.6970 | 0.6765 | 0.5735 |
| 0.198 | 28.3 | 1500 | 0.7448 | 0.3962 | 0.3333 | 0.2857 | 0.4 | 0.3939 | 0.52 | 0.3970 |
| 0.6536 | 30.19 | 1600 | 0.7555 | 0.5094 | 0.4583 | 0.3929 | 0.55 | 0.4848 | 0.64 | 0.5174 |
| 0.7558 | 32.08 | 1700 | 0.6664 | 0.5849 | 0.4762 | 0.4545 | 0.5 | 0.6364 | 0.6774 | 0.5682 |
| 0.4915 | 33.96 | 1800 | 0.9213 | 0.3962 | 0.5152 | 0.3696 | 0.85 | 0.1212 | 0.5714 | 0.4856 |
| 0.3661 | 35.85 | 1900 | 0.9202 | 0.4528 | 0.4912 | 0.3784 | 0.7 | 0.3030 | 0.625 | 0.5015 |
| 0.4838 | 37.74 | 2000 | 0.9297 | 0.4528 | 0.5085 | 0.3846 | 0.75 | 0.2727 | 0.6429 | 0.5114 |
| 0.8461 | 39.62 | 2100 | 0.9464 | 0.4717 | 0.5758 | 0.4130 | 0.95 | 0.1818 | 0.8571 | 0.5659 |
| 0.6937 | 41.51 | 2200 | 0.7129 | 0.5094 | 0.48 | 0.4 | 0.6 | 0.4545 | 0.6522 | 0.5273 |
| 0.6302 | 43.4 | 2300 | 0.6866 | 0.5849 | 0.6071 | 0.4722 | 0.85 | 0.4242 | 0.8235 | 0.6371 |
| 0.0793 | 45.28 | 2400 | 0.7791 | 0.5094 | 0.5517 | 0.4211 | 0.8 | 0.3333 | 0.7333 | 0.5667 |
| 0.464 | 47.17 | 2500 | 0.8116 | 0.4340 | 0.4444 | 0.3529 | 0.6 | 0.3333 | 0.5789 | 0.4667 |
| 0.6131 | 49.06 | 2600 | 0.5970 | 0.6226 | 0.5455 | 0.5 | 0.6 | 0.6364 | 0.7241 | 0.6182 |
| 0.6937 | 50.94 | 2700 | 0.8201 | 0.4340 | 0.4 | 0.3333 | 0.5 | 0.3939 | 0.5652 | 0.4470 |
| 0.6552 | 52.83 | 2800 | 0.7168 | 0.5660 | 0.5306 | 0.4483 | 0.65 | 0.5152 | 0.7083 | 0.5826 |
| 0.7749 | 54.72 | 2900 | 0.6875 | 0.5849 | 0.5217 | 0.4615 | 0.6 | 0.5758 | 0.7037 | 0.5879 |
| 0.9482 | 56.6 | 3000 | 0.6392 | 0.6226 | 0.6296 | 0.5 | 0.85 | 0.4848 | 0.8421 | 0.6674 |
| 0.2467 | 58.49 | 3100 | 0.6281 | 0.6038 | 0.5333 | 0.48 | 0.6 | 0.6061 | 0.7143 | 0.6030 |
| 0.2903 | 60.38 | 3200 | 0.7383 | 0.5472 | 0.5556 | 0.4412 | 0.75 | 0.4242 | 0.7368 | 0.5871 |
| 0.5859 | 62.26 | 3300 | 0.7191 | 0.6226 | 0.5652 | 0.5 | 0.65 | 0.6061 | 0.7407 | 0.6280 |
| 0.3815 | 64.15 | 3400 | 0.7469 | 0.5283 | 0.4444 | 0.4 | 0.5 | 0.5455 | 0.6429 | 0.5227 |
| 0.531 | 66.04 | 3500 | 0.7566 | 0.6226 | 0.5652 | 0.5 | 0.65 | 0.6061 | 0.7407 | 0.6280 |
| 0.3892 | 67.92 | 3600 | 0.8168 | 0.5660 | 0.5490 | 0.4516 | 0.7 | 0.4848 | 0.7273 | 0.5924 |
| 0.6487 | 69.81 | 3700 | 0.9077 | 0.4340 | 0.4643 | 0.3611 | 0.65 | 0.3030 | 0.5882 | 0.4765 |
| 0.5525 | 71.7 | 3800 | 0.6961 | 0.6038 | 0.5116 | 0.4783 | 0.55 | 0.6364 | 0.7 | 0.5932 |
| 0.3137 | 73.58 | 3900 | 1.0817 | 0.3774 | 0.4590 | 0.3415 | 0.7 | 0.1818 | 0.5 | 0.4409 |
| 0.3526 | 75.47 | 4000 | 0.7684 | 0.5472 | 0.5862 | 0.4474 | 0.85 | 0.3636 | 0.8 | 0.6068 |
| 0.5938 | 77.36 | 4100 | 0.8786 | 0.4340 | 0.4828 | 0.3684 | 0.7 | 0.2727 | 0.6 | 0.4864 |
| 0.2431 | 79.25 | 4200 | 0.8925 | 0.4151 | 0.4746 | 0.3590 | 0.7 | 0.2424 | 0.5714 | 0.4712 |
| 0.1021 | 81.13 | 4300 | 1.0740 | 0.4528 | 0.4727 | 0.3714 | 0.65 | 0.3333 | 0.6111 | 0.4917 |
| 0.3429 | 83.02 | 4400 | 0.7723 | 0.4906 | 0.5091 | 0.4 | 0.7 | 0.3636 | 0.6667 | 0.5318 |
| 0.3836 | 84.91 | 4500 | 0.7247 | 0.5472 | 0.5556 | 0.4412 | 0.75 | 0.4242 | 0.7368 | 0.5871 |
| 0.4099 | 86.79 | 4600 | 0.8508 | 0.4340 | 0.4828 | 0.3684 | 0.7 | 0.2727 | 0.6 | 0.4864 |
| 0.8264 | 88.68 | 4700 | 0.7682 | 0.5849 | 0.5769 | 0.4688 | 0.75 | 0.4848 | 0.7619 | 0.6174 |
| 0.1928 | 90.57 | 4800 | 0.8738 | 0.4906 | 0.5574 | 0.4146 | 0.85 | 0.2727 | 0.75 | 0.5614 |
| 0.3422 | 92.45 | 4900 | 0.8810 | 0.5660 | 0.5965 | 0.4595 | 0.85 | 0.3939 | 0.8125 | 0.6220 |
| 0.5524 | 94.34 | 5000 | 1.0801 | 0.3774 | 0.4923 | 0.3556 | 0.8 | 0.1212 | 0.5 | 0.4606 |
| 0.464 | 96.23 | 5100 | 0.9417 | 0.5283 | 0.5902 | 0.4390 | 0.9 | 0.3030 | 0.8333 | 0.6015 |
| 0.7182 | 98.11 | 5200 | 1.0335 | 0.4151 | 0.4746 | 0.3590 | 0.7 | 0.2424 | 0.5714 | 0.4712 |
| 0.604 | 100.0 | 5300 | 0.6947 | 0.6604 | 0.6087 | 0.5385 | 0.7 | 0.6364 | 0.7778 | 0.6682 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
CSResearcher/TestModel | [
"license:mit"
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} | 0 | null | ---
license: apache-2.0
---
Finetune from WangchanBERTa use for Provincial Waterworks Autority of Thailand. |
Callidior/bert2bert-base-arxiv-titlegen | [
"pytorch",
"safetensors",
"encoder-decoder",
"text2text-generation",
"en",
"dataset:arxiv_dataset",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
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} | 145 | null | ---
tags:
- conversational
---
# Melody DialoGPT Model |
Cameron/BERT-Jigsaw | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
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} | 35 | null | ---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_engagement_RoBERTa_combined
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.0
- name: NER Recall
type: recall
value: 0.0
- name: NER F Score
type: f_score
value: 0.0
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.0
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.0
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.0
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.0
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9764065336
---
| Feature | Description |
| --- | --- |
| **Name** | `en_engagement_RoBERTa_combined` |
| **Version** | `AtoI_0.1.85` |
| **spaCy** | `>=3.3.0,<3.4.0` |
| **Default Pipeline** | `transformer`, `tagger`, `parser`, `ner`, `trainable_transformer`, `span_finder`, `spancat` |
| **Components** | `transformer`, `tagger`, `parser`, `ner`, `trainable_transformer`, `span_finder`, `spancat` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (124 labels for 4 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` |
| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
| **`spancat`** | `MONOGLOSS`, `ATTRIBUTE`, `JUSTIFY`, `COUNTER`, `CITATION`, `ENTERTAIN`, `ENDORSE`, `DENY`, `CONCUR`, `PRONOUNCE`, `TEMPORAL`, `CONTRAST` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TAG_ACC` | 0.00 |
| `DEP_UAS` | 0.00 |
| `DEP_LAS` | 0.00 |
| `DEP_LAS_PER_TYPE` | 0.00 |
| `SENTS_P` | 96.76 |
| `SENTS_R` | 98.53 |
| `SENTS_F` | 97.64 |
| `ENTS_F` | 0.00 |
| `ENTS_P` | 0.00 |
| `ENTS_R` | 0.00 |
| `SPAN_FINDER_SPAN_CANDIDATES_F` | 50.09 |
| `SPAN_FINDER_SPAN_CANDIDATES_P` | 35.70 |
| `SPAN_FINDER_SPAN_CANDIDATES_R` | 83.94 |
| `SPANS_SC_F` | 76.49 |
| `SPANS_SC_P` | 75.89 |
| `SPANS_SC_R` | 77.11 |
| `LEMMA_ACC` | 0.00 |
| `TRAINABLE_TRANSFORMER_LOSS` | 1535.39 |
| `SPAN_FINDER_LOSS` | 20411.83 |
| `SPANCAT_LOSS` | 24075.13 | |
dccuchile/albert-large-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
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} | 27 | null | ---
language: en # <-- my language
widget:
- text: " Chinese stocks’ plunge on Monday over fears about China’s new leadership team may be misguided, consulting firm Teneo said. Chinese stocks in Hong Kong and New York, especially internet tech giants such as [TGT], dropped on the first trading day after Chinese President Xi Jinping cemented his firm grip on power with a new core leadership team filled with his loyalists."
- text: "[TGT] stocks dropped 42% while Samsung rallied."
- text: "Tesla stocks dropped 42% while [TGT] rallied."
tags:
- t5
- finbert
- financial-sentiment-analysis
- sentiment-analysis
license:
- apache-2.0
---
## Model Description
FinABSA is a T5-Large model trained for Aspect-Based Sentiment Analysis(ABSA) tasks using [SEntFiN 1.0](https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24634?af=R). Unlike traditional sentiment analysis models which predict a single sentiment label for each sentence, FinABSA has been trained to disambiguate sentences containing multiple aspects. By replacing the target aspect with a [TGT] token the model predicts the sentiment concentrating to the aspect. [GitHub Repo](https://github.com/guijinSON/FinABSA)
## How to use
You can use this model directly using the AutoModelForSeq2SeqLM class.
```python
>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("amphora/FinABSA")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("amphora/FinABSA")
>>> input_str = "[TGT] stocks dropped 42% while Samsung rallied."
>>> input = tokenizer(input_str, return_tensors='pt')
>>> output = model.generate(**input, max_length=20)
>>> print(output)
The sentiment for [TGT] in the given sentence is NEGATIVE.
>>> input_str = "Tesla stocks dropped 42% while [TGT] rallied."
>>> input = tokenizer(input_str, return_tensors='pt')
>>> output = model.generate(**input, max_length=20)
>>> print(output)
The sentiment for [TGT] in the given sentence is POSITIVE.
```
## Evaluation Results
Using a test split arbitarly extracted from [SEntFiN 1.0](https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24634?af=R) the model scores an average accuracy of 87%. |
dccuchile/albert-tiny-spanish-finetuned-ner | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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} | 8 | null | ---
license: openrail
library_name: diffusers
tags:
- TPU
- JAX
- Flax
- stable-diffusion
- text-to-image
language:
- en
---
|
dccuchile/albert-tiny-spanish-finetuned-xnli | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
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} | 31 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-distilroberta-base-finetuned-SarcojiComplEmojisDistilRobertaMLM
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-distilroberta-base-finetuned-SarcojiComplEmojisDistilRobertaMLM
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8538
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 191 | 2.9461 |
| No log | 2.0 | 382 | 2.8536 |
| 3.0333 | 3.0 | 573 | 2.8745 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
dccuchile/albert-xlarge-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
]
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} | 7 | null | ---
license: mit
tags:
- audio
- music
- generation
- tensorflow
---
# Musika Model: musika_anime_songs
## Model provided by: djquma
Pretrained musika_anime_songs model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation.
Introduced in [this paper](https://arxiv.org/abs/2208.08706).
## How to use
You can generate music from this pretrained musika_anime_songs model using the notebook available [here](https://colab.research.google.com/drive/1HJWliBXPi-Xlx3gY8cjFI5-xaZgrTD7r).
### Model description
This pretrained GAN system consists of a ResNet-style generator and discriminator. During training, stability is controlled by adapting the strength of gradient penalty regularization on-the-fly. The gradient penalty weighting term is contained in *switch.npy*. The generator is conditioned on a latent coordinate system to produce samples of arbitrary length. The latent representations produced by the generator are then passed to a decoder which converts them into waveform audio.
The generator has a context window of about 12 seconds of audio.
|
dccuchile/albert-xxlarge-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
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} | 26 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9405
- name: F1
type: f1
value: 0.9408676491029256
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1465
- Accuracy: 0.9405
- F1: 0.9409
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8341 | 1.0 | 250 | 0.2766 | 0.9105 | 0.9088 |
| 0.2181 | 2.0 | 500 | 0.1831 | 0.9305 | 0.9308 |
| 0.141 | 3.0 | 750 | 0.1607 | 0.93 | 0.9305 |
| 0.1102 | 4.0 | 1000 | 0.1509 | 0.935 | 0.9344 |
| 0.0908 | 5.0 | 1250 | 0.1465 | 0.9405 | 0.9409 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
dccuchile/albert-xxlarge-spanish-finetuned-ner | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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} | 28 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- scitldr
metrics:
- rouge
model-index:
- name: paper-summary
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: scitldr
type: scitldr
config: Abstract
split: train
args: Abstract
metrics:
- name: Rouge1
type: rouge
value: 0.3484
---
<!-- 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. -->
# paper-summary
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the scitldr dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8631
- Rouge1: 0.3484
- Rouge2: 0.1596
- Rougel: 0.2971
- Rougelsum: 0.3047
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 3.0545 | 1.0 | 63 | 2.9939 | 0.3387 | 0.1538 | 0.2887 | 0.2957 |
| 2.7871 | 2.0 | 126 | 2.9360 | 0.3448 | 0.1577 | 0.2947 | 0.3019 |
| 2.7188 | 3.0 | 189 | 2.8977 | 0.3477 | 0.1585 | 0.2967 | 0.3035 |
| 2.6493 | 4.0 | 252 | 2.8837 | 0.3488 | 0.1597 | 0.2973 | 0.3046 |
| 2.6207 | 5.0 | 315 | 2.8690 | 0.3472 | 0.1566 | 0.2958 | 0.3033 |
| 2.5893 | 6.0 | 378 | 2.8668 | 0.3493 | 0.1592 | 0.2972 | 0.305 |
| 2.5494 | 7.0 | 441 | 2.8657 | 0.3486 | 0.1595 | 0.2976 | 0.3053 |
| 2.5554 | 8.0 | 504 | 2.8631 | 0.3484 | 0.1596 | 0.2971 | 0.3047 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
dccuchile/albert-xxlarge-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
]
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} | 42 | null | ---
tags:
- generated_from_trainer
model-index:
- name: bigbird-roberta-large-fever
results: []
datasets:
- copenlu/fever_gold_evidence
---
<!-- 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. -->
# bigbird-roberta-large-fever
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4721
- eval_p: 0.8933
- eval_r: 0.8930
- eval_f1: 0.8926
- eval_runtime: 153.523
- eval_samples_per_second: 122.49
- eval_steps_per_second: 15.314
- 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: 1e-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: cosine
- num_epochs: 4
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.12.1
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-pawsx | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 25 | null | # Randeng-PPVAE-1.2B-Augmentation-Chinese
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/models/PPVAE)
- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/zh/latest/docs/%E7%87%83%E7%81%AF%E7%B3%BB%E5%88%97/Randeng-PPVAE-1.2B-Augmentation-Chinese.html)
## 简介 Brief Introduction
PPVAE(Pre-train and Plug-in Variational Auto-Encoder) 可以通过少量类别文本的训练生成大量该类别的增强样本。
PPVAE是一个由两个VAE组成的层级框架：预训练VAE的编码器得到文本全局隐空间，解码器将隐向量解码为文本；PluginVAE为一个轻量级VAE，学习从全局隐空间到条件隐空间的相互映射，该映射只需要少量条件文本即可训练完成。
PPVAE (Pre-train and Plug-in Variational Auto-Encoder) can generate a large number of category-specific samples from the training of a small number of category texts. PPVAE is a hierarchical framework consisting of two VAEs: the encoder of the pre-trained VAE gets the text global hidden space and the decoder decodes the hidden vector into text; PluginVAE is a lightweight VAE that learns the transformation from the global hidden space to the conditional hidden space, which requires only a small amount of conditional text to be trained.
PPVAE参考论文[Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders](https://arxiv.org/abs/1911.03882).
PPVAE reference paper [Pre-training and Plug-in: Flexible Conditional Text Generation with Variable Autoencoders](https://arxiv.org/abs/1911.03882).
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 数据增强 Augmentation | 自然语言生成 NLG | 燃灯 Randeng | VAE | 1.2B | pluginVAE |
## 模型信息 Model Information
**Pretrained VAE:**
训练语料：悟道语料库（280G版本）
Training Corpus: Wudao Corpus (with 280G samples)
参考模型：[Randeng-DAVAE-1.2B-General-Chinese](https://huggingface.co/IDEA-CCNL/Randeng-DAVAE-1.2B-General-Chinese)
Reference model:[Randeng-DAVAE-1.2B-General-Chinese](https://huggingface.co/IDEA-CCNL/Randeng-DAVAE-1.2B-General-Chinese)
**PluginVAE:**
编码器：三层MLP，将隐向量从全局隐空间映射到类别隐空间；
解码器：三层MLP，将隐向量从类别隐空间映射到全局隐空间。
训练语料：少量类别文本。
Encoder: three-layer MLP that maps the hidden vector from the global hidden space to the category hidden space.
Decoder: three-layer MLP, mapping hidden vectors from the category hidden space to the global hidden space.
Training corpus: a small amount of categorical text.
## 使用 Usage
```shell
git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git
cd Fengshenbang-LM
pip install --editable .
```
```python3
import torch
from transformers import BertTokenizer,T5Tokenizer
from fengshen.models.PPVAE.pluginVAE import PPVAEModel
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
input_texts = [
"非常好的一个博物馆，是我所有去过的博物馆里感觉最正规的一家.",
"这是我来长沙最最期待的一定要去的地方，总算今天特地去瞻仰千古遗容了，真好。",
"地方很大 很气派~~可以逛很久~~~去的时候是免费的~不过要安检~~~里面的马王堆~幸追夫人~还是很不错的",
"绝对不虚此行！相当震撼的展览！原来古人也化妆，还有假发。记忆最深的是那个藕汤。可惜真颜已不得见。",
"去过三次，个人认为这是长沙最值得去的地方.",
"非常喜欢的一家博物馆，里面可看的东西很多，当然最吸引我的就是那个辛追夫人和“素纱单衣”，果然不是盖的~赞~~~",
"这两年也有很多机会去博物馆。。。不过还是想说湖南省博物馆是非常有特色的。。。真是上了一节很生动的历史课。",
"网上订票去的，还是很顺利的就进去了，里面挺清净的，外围的环境也不错，还有鸽子可以喂。",
]
encoder_tokenizer = BertTokenizer.from_pretrained("IDEA-CCNL/Randeng-PPVAE-1.2B-Augmentation-Chinese")
decoder_tokenizer = T5Tokenizer.from_pretrained("IDEA-CCNL/Randeng-PPVAE-1.2B-Augmentation-Chinese", eos_token = '<|endoftext|>', pad_token = '<pad>',extra_ids=0)
decoder_tokenizer.add_special_tokens({'bos_token':'<bos>'})
ppvae_model = PPVAEModel.from_pretrained("IDEA-CCNL/Randeng-PPVAE-1.2B-Augmentation-Chinese").to(device)
ppvae_model.train_plugin(encoder_tokenizer,decoder_tokenizer,input_texts,negative_samples=None)
# n:输出样本数量
texts = ppvae_model.generate(n=5)
print(texts)
# 生成结果样例：
# ['同学很推荐那里,自然会有好的风景.那里物价很便宜,真的不错。',
# '同学说一会去盛国,可能是我去的比较多!故居真的很漂亮,夜景也特别好看。'
# '我的第一次旅行没有白来,最后领略了有些风吹草低见牛羊的味道,谢谢本次疗养。',
# '同学一打听:这里距离世纪公园,还有最近的香山营不过200米,海拔也才四千米。',
# '我发现那边很文艺!!有机会去过的,真是土耳其当地口音~还是很干净!。', ]
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
If you are using the resource for your work, please cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
``` |
dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
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} | 5 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: Galeros/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-xnli | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 28 | null | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: uk
datasets:
- ner-uk
model-index:
- name: flair-uk-ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8616
- name: NER Recall
type: recall
value: 0.8593
- name: NER F Score
type: f_score
value: 0.8605
widget:
- text: "Президент Володимир Зеленський пояснив, що наразі діалог із режимом Володимира путіна неможливий, адже агресор обрав курс на знищення українського народу. За словами Зеленського цей режим РФ виявляє неповагу до суверенітету і територіальної цілісності України."
license: mit
---
# flair-uk-ner
## Model description
**flair-uk-ner** is a Flair model that is ready to use for **Named Entity Recognition**. It is based on flair embeddings, that I've trained for Ukrainian language (available [here](https://huggingface.co/dchaplinsky/flair-uk-backward) and [here](https://huggingface.co/dchaplinsky/flair-uk-forward)) and has nice performance and a very **small size** (just 72mb!).
It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PERS) and Miscellaneous (MISC).
Results:
- F-score (micro) **0.8605**
- F-score (macro) **0.7472**
- Accuracy **0.8033**
| by class | precision | recall | f1-score | support |
|--------------|-----------|--------|----------|---------|
| **PERS** | 0.9305 | 0.9422 | 0.9363 | 1678 |
| **LOC** | 0.8150 | 0.8678 | 0.8406 | 401 |
| **ORG** | 0.6653 | 0.6092 | 0.6360 | 261 |
| **MISC** | 0.6202 | 0.5375 | 0.5759 | 240 |
| micro avg | 0.8616 | 0.8593 | 0.8605 | 2580 |
| macro avg | 0.7577 | 0.7392 | 0.7472 | 2580 |
| weighted avg | 0.8569 | 0.8593 | 0.8575 | 2580 |
The model was fine-tuned on the [NER-UK dataset](https://github.com/lang-uk/ner-uk), released by the [lang-uk](https://lang.org.ua).
Training code is also available [here](https://github.com/lang-uk/flair-ner).
Copyright: [Dmytro Chaplynskyi](https://twitter.com/dchaplinsky), [lang-uk project](https://lang.org.ua), 2022 |
dccuchile/bert-base-spanish-wwm-uncased-finetuned-mldoc | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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} | 39 | null | ---
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-sroie
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. -->
# donut-base-sroie
This model is a fine-tuned version of [](https://huggingface.co/) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.8.2+cu111
- Datasets 2.6.1
- Tokenizers 0.13.2
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-pos | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.5266
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 7 | 5.3892 |
| No log | 2.0 | 14 | 4.7949 |
| No log | 3.0 | 21 | 4.5266 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.10.1+cu102
- Datasets 2.6.1
- Tokenizers 0.12.1
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-qa-mlqa | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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"BertForQuestionAnswering"
],
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} | 5 | null | ---
language:
- zh
tags:
- pytorch
- zh
---
[hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) fine-tuned on the [COLDataset](https://github.com/thu-coai/COLDataset). Usage example:
```python
import torch
from transformers.models.bert import BertTokenizer, BertForSequenceClassification
tokenizer = BertTokenizer.from_pretrained('thu-coai/roberta-base-cold')
model = BertForSequenceClassification.from_pretrained('thu-coai/roberta-base-cold')
model.eval()
texts = ['你就是个傻逼！','黑人很多都好吃懒做，偷奸耍滑！','男女平等，黑人也很优秀。']
model_input = tokenizer(texts,return_tensors="pt",padding=True)
model_output = model(**model_input, return_dict=False)
prediction = torch.argmax(model_output[0].cpu(), dim=-1)
prediction = [p.item() for p in prediction]
print(prediction) # --> [1, 1, 0] (0 for Non-Offensive, 1 for Offenisve)
```
This fine-tuned model obtains 82.75 accuracy and 82.39 macro-F1 on the test set.
Please kindly cite the [original paper](https://arxiv.org/abs/2201.06025) if you use this model.
```
@article{deng2022cold,
title={Cold: A benchmark for chinese offensive language detection},
author={Deng, Jiawen and Zhou, Jingyan and Sun, Hao and Zheng, Chujie and Mi, Fei and Meng, Helen and Huang, Minlie},
booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
year={2022}
}
```
|
dccuchile/distilbert-base-spanish-uncased-finetuned-ner | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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} | 28 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_polarity
metrics:
- accuracy
model-index:
- name: amazonPolarity_ELECTRA_5E
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_polarity
type: amazon_polarity
config: amazon_polarity
split: train
args: amazon_polarity
metrics:
- name: Accuracy
type: accuracy
value: 0.9333333333333333
---
<!-- 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. -->
# amazonPolarity_ELECTRA_5E
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the amazon_polarity dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3512
- Accuracy: 0.9333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6705 | 0.03 | 50 | 0.5768 | 0.8867 |
| 0.4054 | 0.05 | 100 | 0.2968 | 0.8933 |
| 0.2461 | 0.08 | 150 | 0.2233 | 0.92 |
| 0.1795 | 0.11 | 200 | 0.2265 | 0.9333 |
| 0.2293 | 0.13 | 250 | 0.2329 | 0.9267 |
| 0.1541 | 0.16 | 300 | 0.2240 | 0.94 |
| 0.2006 | 0.19 | 350 | 0.2779 | 0.92 |
| 0.1826 | 0.21 | 400 | 0.2765 | 0.9133 |
| 0.1935 | 0.24 | 450 | 0.2346 | 0.9333 |
| 0.1887 | 0.27 | 500 | 0.2085 | 0.94 |
| 0.1688 | 0.29 | 550 | 0.2193 | 0.94 |
| 0.1884 | 0.32 | 600 | 0.1982 | 0.9467 |
| 0.189 | 0.35 | 650 | 0.1873 | 0.94 |
| 0.1564 | 0.37 | 700 | 0.2226 | 0.94 |
| 0.1733 | 0.4 | 750 | 0.2462 | 0.9333 |
| 0.1436 | 0.43 | 800 | 0.2328 | 0.94 |
| 0.1517 | 0.45 | 850 | 0.2128 | 0.9533 |
| 0.1922 | 0.48 | 900 | 0.1626 | 0.9467 |
| 0.1401 | 0.51 | 950 | 0.2391 | 0.94 |
| 0.1606 | 0.53 | 1000 | 0.2001 | 0.94 |
| 0.1597 | 0.56 | 1050 | 0.1788 | 0.9467 |
| 0.184 | 0.59 | 1100 | 0.1656 | 0.9467 |
| 0.1448 | 0.61 | 1150 | 0.1752 | 0.96 |
| 0.1575 | 0.64 | 1200 | 0.1878 | 0.9533 |
| 0.1836 | 0.67 | 1250 | 0.1416 | 0.9533 |
| 0.1378 | 0.69 | 1300 | 0.1866 | 0.9467 |
| 0.1901 | 0.72 | 1350 | 0.1654 | 0.9533 |
| 0.1697 | 0.75 | 1400 | 0.1720 | 0.9533 |
| 0.1624 | 0.77 | 1450 | 0.1700 | 0.9467 |
| 0.1487 | 0.8 | 1500 | 0.1786 | 0.94 |
| 0.1367 | 0.83 | 1550 | 0.1974 | 0.9267 |
| 0.1535 | 0.85 | 1600 | 0.1823 | 0.9267 |
| 0.1366 | 0.88 | 1650 | 0.1515 | 0.94 |
| 0.1505 | 0.91 | 1700 | 0.1527 | 0.94 |
| 0.1554 | 0.93 | 1750 | 0.1855 | 0.9467 |
| 0.1478 | 0.96 | 1800 | 0.1885 | 0.9333 |
| 0.1603 | 0.99 | 1850 | 0.1990 | 0.9467 |
| 0.1637 | 1.01 | 1900 | 0.1901 | 0.9467 |
| 0.1074 | 1.04 | 1950 | 0.1886 | 0.9533 |
| 0.0874 | 1.07 | 2000 | 0.2399 | 0.94 |
| 0.1245 | 1.09 | 2050 | 0.2107 | 0.9467 |
| 0.1175 | 1.12 | 2100 | 0.2226 | 0.94 |
| 0.1279 | 1.15 | 2150 | 0.2267 | 0.94 |
| 0.0947 | 1.17 | 2200 | 0.2342 | 0.94 |
| 0.0837 | 1.2 | 2250 | 0.2519 | 0.9467 |
| 0.1091 | 1.23 | 2300 | 0.2531 | 0.94 |
| 0.0867 | 1.25 | 2350 | 0.2519 | 0.94 |
| 0.0845 | 1.28 | 2400 | 0.2431 | 0.9467 |
| 0.0836 | 1.31 | 2450 | 0.1936 | 0.9533 |
| 0.1633 | 1.33 | 2500 | 0.1875 | 0.9333 |
| 0.1029 | 1.36 | 2550 | 0.2345 | 0.94 |
| 0.0755 | 1.39 | 2600 | 0.3028 | 0.94 |
| 0.1539 | 1.41 | 2650 | 0.2497 | 0.94 |
| 0.1055 | 1.44 | 2700 | 0.2002 | 0.9467 |
| 0.1234 | 1.47 | 2750 | 0.1763 | 0.9533 |
| 0.1312 | 1.49 | 2800 | 0.1998 | 0.94 |
| 0.1067 | 1.52 | 2850 | 0.1820 | 0.96 |
| 0.1092 | 1.55 | 2900 | 0.1903 | 0.9467 |
| 0.1209 | 1.57 | 2950 | 0.1912 | 0.9467 |
| 0.0627 | 1.6 | 3000 | 0.2208 | 0.9467 |
| 0.1121 | 1.63 | 3050 | 0.2607 | 0.9333 |
| 0.1106 | 1.65 | 3100 | 0.1852 | 0.9533 |
| 0.0724 | 1.68 | 3150 | 0.2122 | 0.9533 |
| 0.1247 | 1.71 | 3200 | 0.2112 | 0.9467 |
| 0.1247 | 1.73 | 3250 | 0.2021 | 0.9533 |
| 0.096 | 1.76 | 3300 | 0.2340 | 0.9467 |
| 0.1056 | 1.79 | 3350 | 0.2165 | 0.94 |
| 0.1055 | 1.81 | 3400 | 0.2563 | 0.94 |
| 0.1199 | 1.84 | 3450 | 0.2251 | 0.9467 |
| 0.0899 | 1.87 | 3500 | 0.1996 | 0.9533 |
| 0.109 | 1.89 | 3550 | 0.1924 | 0.9533 |
| 0.13 | 1.92 | 3600 | 0.1769 | 0.9467 |
| 0.1037 | 1.95 | 3650 | 0.2003 | 0.9533 |
| 0.0934 | 1.97 | 3700 | 0.2325 | 0.94 |
| 0.1254 | 2.0 | 3750 | 0.2037 | 0.9467 |
| 0.0619 | 2.03 | 3800 | 0.2252 | 0.9533 |
| 0.093 | 2.05 | 3850 | 0.2145 | 0.9533 |
| 0.0827 | 2.08 | 3900 | 0.2237 | 0.9533 |
| 0.0679 | 2.11 | 3950 | 0.2643 | 0.9467 |
| 0.076 | 2.13 | 4000 | 0.2287 | 0.9533 |
| 0.0526 | 2.16 | 4050 | 0.3210 | 0.9267 |
| 0.0354 | 2.19 | 4100 | 0.3259 | 0.9333 |
| 0.026 | 2.21 | 4150 | 0.3448 | 0.9333 |
| 0.0466 | 2.24 | 4200 | 0.3751 | 0.9333 |
| 0.043 | 2.27 | 4250 | 0.3122 | 0.9333 |
| 0.0521 | 2.29 | 4300 | 0.3155 | 0.9333 |
| 0.1018 | 2.32 | 4350 | 0.3066 | 0.94 |
| 0.0572 | 2.35 | 4400 | 0.2848 | 0.94 |
| 0.0903 | 2.37 | 4450 | 0.2289 | 0.9467 |
| 0.0718 | 2.4 | 4500 | 0.2661 | 0.9467 |
| 0.0689 | 2.43 | 4550 | 0.2544 | 0.9467 |
| 0.0829 | 2.45 | 4600 | 0.2816 | 0.9333 |
| 0.0909 | 2.48 | 4650 | 0.2244 | 0.94 |
| 0.0888 | 2.51 | 4700 | 0.2620 | 0.94 |
| 0.0998 | 2.53 | 4750 | 0.2773 | 0.94 |
| 0.0604 | 2.56 | 4800 | 0.2344 | 0.94 |
| 0.0619 | 2.59 | 4850 | 0.2551 | 0.9467 |
| 0.056 | 2.61 | 4900 | 0.2787 | 0.94 |
| 0.1037 | 2.64 | 4950 | 0.2388 | 0.9467 |
| 0.0858 | 2.67 | 5000 | 0.2213 | 0.94 |
| 0.0674 | 2.69 | 5050 | 0.2339 | 0.9467 |
| 0.0438 | 2.72 | 5100 | 0.2759 | 0.9467 |
| 0.0615 | 2.75 | 5150 | 0.2739 | 0.9467 |
| 0.064 | 2.77 | 5200 | 0.2488 | 0.9467 |
| 0.0824 | 2.8 | 5250 | 0.2590 | 0.9467 |
| 0.074 | 2.83 | 5300 | 0.2314 | 0.9467 |
| 0.1077 | 2.85 | 5350 | 0.2571 | 0.9467 |
| 0.0482 | 2.88 | 5400 | 0.2678 | 0.9467 |
| 0.0732 | 2.91 | 5450 | 0.2626 | 0.9333 |
| 0.0564 | 2.93 | 5500 | 0.2586 | 0.94 |
| 0.1019 | 2.96 | 5550 | 0.2706 | 0.9333 |
| 0.0675 | 2.99 | 5600 | 0.2568 | 0.9267 |
| 0.056 | 3.01 | 5650 | 0.2881 | 0.9333 |
| 0.0266 | 3.04 | 5700 | 0.2789 | 0.9467 |
| 0.0207 | 3.07 | 5750 | 0.2535 | 0.9467 |
| 0.0246 | 3.09 | 5800 | 0.2597 | 0.9467 |
| 0.0631 | 3.12 | 5850 | 0.2403 | 0.9533 |
| 0.0627 | 3.15 | 5900 | 0.2336 | 0.9533 |
| 0.1061 | 3.17 | 5950 | 0.2773 | 0.94 |
| 0.0257 | 3.2 | 6000 | 0.2587 | 0.9467 |
| 0.0375 | 3.23 | 6050 | 0.2560 | 0.9467 |
| 0.0404 | 3.25 | 6100 | 0.2851 | 0.94 |
| 0.0748 | 3.28 | 6150 | 0.3005 | 0.94 |
| 0.0384 | 3.31 | 6200 | 0.2442 | 0.9533 |
| 0.0426 | 3.33 | 6250 | 0.2618 | 0.9533 |
| 0.0611 | 3.36 | 6300 | 0.2710 | 0.9467 |
| 0.0282 | 3.39 | 6350 | 0.3200 | 0.94 |
| 0.0449 | 3.41 | 6400 | 0.3203 | 0.94 |
| 0.0508 | 3.44 | 6450 | 0.3197 | 0.94 |
| 0.0385 | 3.47 | 6500 | 0.3391 | 0.9333 |
| 0.0458 | 3.49 | 6550 | 0.3450 | 0.9333 |
| 0.0245 | 3.52 | 6600 | 0.3737 | 0.9333 |
| 0.0547 | 3.55 | 6650 | 0.2889 | 0.94 |
| 0.0398 | 3.57 | 6700 | 0.3751 | 0.9333 |
| 0.0497 | 3.6 | 6750 | 0.2748 | 0.9467 |
| 0.0466 | 3.63 | 6800 | 0.3438 | 0.9333 |
| 0.0241 | 3.65 | 6850 | 0.3279 | 0.9267 |
| 0.0631 | 3.68 | 6900 | 0.2921 | 0.94 |
| 0.0256 | 3.71 | 6950 | 0.3595 | 0.9267 |
| 0.0615 | 3.73 | 7000 | 0.3190 | 0.9333 |
| 0.0495 | 3.76 | 7050 | 0.3451 | 0.9267 |
| 0.0519 | 3.79 | 7100 | 0.3303 | 0.9333 |
| 0.0243 | 3.81 | 7150 | 0.3344 | 0.9333 |
| 0.0348 | 3.84 | 7200 | 0.3609 | 0.9333 |
| 0.0542 | 3.87 | 7250 | 0.2797 | 0.9333 |
| 0.0791 | 3.89 | 7300 | 0.2504 | 0.94 |
| 0.0272 | 3.92 | 7350 | 0.3165 | 0.9333 |
| 0.0701 | 3.95 | 7400 | 0.3039 | 0.9333 |
| 0.0866 | 3.97 | 7450 | 0.3233 | 0.9267 |
| 0.0461 | 4.0 | 7500 | 0.3114 | 0.9267 |
| 0.0486 | 4.03 | 7550 | 0.2995 | 0.94 |
| 0.0052 | 4.05 | 7600 | 0.3128 | 0.94 |
| 0.0312 | 4.08 | 7650 | 0.3723 | 0.9333 |
| 0.0277 | 4.11 | 7700 | 0.3158 | 0.94 |
| 0.0407 | 4.13 | 7750 | 0.3187 | 0.94 |
| 0.0224 | 4.16 | 7800 | 0.3258 | 0.9333 |
| 0.0335 | 4.19 | 7850 | 0.3539 | 0.9333 |
| 0.0425 | 4.21 | 7900 | 0.3391 | 0.9333 |
| 0.0394 | 4.24 | 7950 | 0.3470 | 0.9333 |
| 0.015 | 4.27 | 8000 | 0.3680 | 0.9333 |
| 0.0166 | 4.29 | 8050 | 0.3689 | 0.9333 |
| 0.0358 | 4.32 | 8100 | 0.3281 | 0.94 |
| 0.0152 | 4.35 | 8150 | 0.3391 | 0.9333 |
| 0.0235 | 4.37 | 8200 | 0.3506 | 0.94 |
| 0.0357 | 4.4 | 8250 | 0.3549 | 0.94 |
| 0.0153 | 4.43 | 8300 | 0.3564 | 0.94 |
| 0.0366 | 4.45 | 8350 | 0.3836 | 0.9333 |
| 0.0381 | 4.48 | 8400 | 0.3428 | 0.9333 |
| 0.0349 | 4.51 | 8450 | 0.3600 | 0.94 |
| 0.028 | 4.53 | 8500 | 0.3592 | 0.9333 |
| 0.0322 | 4.56 | 8550 | 0.3478 | 0.9333 |
| 0.0237 | 4.59 | 8600 | 0.3636 | 0.94 |
| 0.0398 | 4.61 | 8650 | 0.3433 | 0.9333 |
| 0.062 | 4.64 | 8700 | 0.3158 | 0.94 |
| 0.0148 | 4.67 | 8750 | 0.3435 | 0.9333 |
| 0.0197 | 4.69 | 8800 | 0.3394 | 0.9333 |
| 0.0594 | 4.72 | 8850 | 0.3336 | 0.9333 |
| 0.0426 | 4.75 | 8900 | 0.3351 | 0.9333 |
| 0.003 | 4.77 | 8950 | 0.3479 | 0.9333 |
| 0.0268 | 4.8 | 9000 | 0.3479 | 0.9333 |
| 0.0524 | 4.83 | 9050 | 0.3485 | 0.9333 |
| 0.0259 | 4.85 | 9100 | 0.3501 | 0.9333 |
| 0.0326 | 4.88 | 9150 | 0.3498 | 0.9333 |
| 0.0236 | 4.91 | 9200 | 0.3482 | 0.9333 |
| 0.0209 | 4.93 | 9250 | 0.3504 | 0.9333 |
| 0.0366 | 4.96 | 9300 | 0.3503 | 0.9333 |
| 0.0246 | 4.99 | 9350 | 0.3512 | 0.9333 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
dccuchile/distilbert-base-spanish-uncased-finetuned-pos | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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} | 3 | null | ---
pipeline_tag: text-classification
widget:
- text: "Pani Katarzyno z jakiej racji moja paczka przyszła do sąsiada zamiast do mnie? Nie można poprawnie nadać paczki?"
example_title: "Sentiment"
license: cc-by-4.0
language:
- pl
---
<img src="https://public.3.basecamp.com/p/rs5XqmAuF1iEuW6U7nMHcZeY/upload/download/VL-NLP-short.png" alt="logo voicelab nlp" style="width:300px;"/>
# Sentiment Classification in Polish
```python
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification
id2label = {0: "negative", 1: "neutral", 2: "positive"}
tokenizer = AutoTokenizer.from_pretrained("Voicelab/herbert-base-cased-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("Voicelab/herbert-base-cased-sentiment")
input = ["Ale fajnie, spadł dzisiaj śnieg! Ulepimy dziś bałwana?"]
encoding = tokenizer(
input,
add_special_tokens=True,
return_token_type_ids=True,
truncation=True,
padding='max_length',
return_attention_mask=True,
return_tensors='pt',
)
output = model(**encoding).logits.to("cpu").detach().numpy()
prediction = id2label[np.argmax(output)]
print(input, "--->", prediction)
```
Predicted output:
```python
['Ale fajnie, spadł dzisiaj śnieg! Ulepimy dziś bałwana?'] ---> positive
```
### Overview
- **Language model:** [allegro/herbert-base-cased](https://huggingface.co/allegro/herbert-base-cased)
- **Language:** pl
- **Training data:** Reviews + own data
- **Blog post:** [Sentiment analysis - COVID-19 – the source of the heated discussion](https://voicelab.ai/covid-19-the-source-of-the-heated-discussion)
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 7 | 2022-11-02T10:26:12Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: twitter-data-xlm-roberta-base-eng-only-sentiment-finetuned-memes
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# twitter-data-xlm-roberta-base-eng-only-sentiment-finetuned-memes
This model is a fine-tuned version of [jayantapaul888/twitter-data-xlm-roberta-base-sentiment-finetuned-memes](https://huggingface.co/jayantapaul888/twitter-data-xlm-roberta-base-sentiment-finetuned-memes) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6286
- Accuracy: 0.8660
- Precision: 0.8796
- Recall: 0.8795
- F1: 0.8795
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 378 | 0.3421 | 0.8407 | 0.8636 | 0.8543 | 0.8553 |
| 0.396 | 2.0 | 756 | 0.3445 | 0.8496 | 0.8726 | 0.8634 | 0.8631 |
| 0.2498 | 3.0 | 1134 | 0.3656 | 0.8585 | 0.8764 | 0.8727 | 0.8723 |
| 0.1543 | 4.0 | 1512 | 0.4549 | 0.8600 | 0.8742 | 0.8740 | 0.8741 |
| 0.1543 | 5.0 | 1890 | 0.5932 | 0.8645 | 0.8783 | 0.8780 | 0.8780 |
| 0.0815 | 6.0 | 2268 | 0.6286 | 0.8660 | 0.8796 | 0.8795 | 0.8795 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Chaewon/mnmt_decoder_en | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 8 | 2022-11-02T10:50:59Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 400 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 400,
"warmup_steps": 40,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Chaima/TunBerto | []
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} | 0 | 2022-11-02T11:11:28Z | ---
license: cc
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: racism-finetuned-detests-02-11-2022
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. -->
# racism-finetuned-detests-02-11-2022
This model is a fine-tuned version of [davidmasip/racism](https://huggingface.co/davidmasip/racism) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8819
- F1: 0.6199
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3032 | 0.64 | 25 | 0.3482 | 0.6434 |
| 0.1132 | 1.28 | 50 | 0.3707 | 0.6218 |
| 0.1253 | 1.92 | 75 | 0.4004 | 0.6286 |
| 0.0064 | 2.56 | 100 | 0.6223 | 0.6254 |
| 0.0007 | 3.21 | 125 | 0.7347 | 0.6032 |
| 0.0006 | 3.85 | 150 | 0.7705 | 0.6312 |
| 0.0004 | 4.49 | 175 | 0.7988 | 0.6304 |
| 0.0003 | 5.13 | 200 | 0.8206 | 0.6255 |
| 0.0003 | 5.77 | 225 | 0.8371 | 0.6097 |
| 0.0003 | 6.41 | 250 | 0.8503 | 0.6148 |
| 0.0003 | 7.05 | 275 | 0.8610 | 0.6148 |
| 0.0002 | 7.69 | 300 | 0.8693 | 0.6199 |
| 0.0002 | 8.33 | 325 | 0.8755 | 0.6199 |
| 0.0002 | 8.97 | 350 | 0.8797 | 0.6199 |
| 0.0002 | 9.62 | 375 | 0.8819 | 0.6199 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Champion/test_upload_vox2_wavlm_epoch8 | [
"sidekit",
"audio"
]
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} | 0 | 2022-11-02T11:50:15Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- lg-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: luganda-ner-v1
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: lg-ner
type: lg-ner
config: lug
split: test
args: lug
metrics:
- name: Precision
type: precision
value: 0.7987967914438503
- name: Recall
type: recall
value: 0.8025520483546004
- name: F1
type: f1
value: 0.8006700167504188
- name: Accuracy
type: accuracy
value: 0.9451502831421519
---
<!-- 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. -->
# luganda-ner-v1
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the lg-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3188
- Precision: 0.7988
- Recall: 0.8026
- F1: 0.8007
- Accuracy: 0.9452
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 261 | 0.2915 | 0.7456 | 0.6891 | 0.7162 | 0.9240 |
| 0.2284 | 2.0 | 522 | 0.2965 | 0.7393 | 0.7314 | 0.7353 | 0.9294 |
| 0.2284 | 3.0 | 783 | 0.2830 | 0.7426 | 0.7576 | 0.7500 | 0.9271 |
| 0.1426 | 4.0 | 1044 | 0.2710 | 0.7935 | 0.7690 | 0.7810 | 0.9387 |
| 0.1426 | 5.0 | 1305 | 0.2805 | 0.8087 | 0.7636 | 0.7855 | 0.9389 |
| 0.0881 | 6.0 | 1566 | 0.2992 | 0.7734 | 0.7884 | 0.7808 | 0.9404 |
| 0.0881 | 7.0 | 1827 | 0.2746 | 0.8109 | 0.7864 | 0.7985 | 0.9457 |
| 0.0582 | 8.0 | 2088 | 0.3149 | 0.7753 | 0.7925 | 0.7838 | 0.9400 |
| 0.0582 | 9.0 | 2349 | 0.3179 | 0.7940 | 0.7945 | 0.7942 | 0.9440 |
| 0.0403 | 10.0 | 2610 | 0.3188 | 0.7988 | 0.8026 | 0.8007 | 0.9452 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Chan/distilgpt2-finetuned-wikitext2 | []
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} | 0 | 2022-11-02T11:56:14Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: jbk
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 `jbk` 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/jbk1/ddpm-butterflies-128/tensorboard?#scalars)
|
Cheapestmedsshop/Buymodafinilus | []
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} | 0 | 2022-11-02T12:30:32Z | # SentiWSP
## For paper: Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis
We propose **SentiWSP**, a novel **Senti**ment-aware pre-trained language model with combined **W**ord-level and **S**entence-level **P**re-training tasks.
The word level pre-training task detects replaced sentiment words, via a generator-discriminator framework, to enhance the PLM's knowledge about sentiment words.
The sentence level pre-training task further strengthens the discriminator via a contrastive learning framework, with similar sentences as negative samples, to encode sentiments in a sentence.
## Fine-tunning
You can also load our model in huggingface ([https://huggingface.co/shuaifan/SentiWSP-base](https://huggingface.co/shuaifan/SentiWSP-base)) to fine-tunning in sentiment analysis tasks:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("shuaifan/SentiWSP-base")
model = AutoModelForSequenceClassification.from_pretrained("shuaifan/SentiWSP-base")
``` |
Cheatham/xlm-roberta-large-finetuned3 | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
]
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} | 22 | 2022-11-02T12:55:14Z | ---
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
config: PAN-X.de
split: train
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8648740833380706
---
<!-- 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: 0.1365
- F1: 0.8649
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 |
| 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 |
| 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Chinat/test-classifier | []
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} | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 80 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 80,
"warmup_steps": 8,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Ching/negation_detector | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
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},
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} | 9 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 465 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 465,
"warmup_steps": 47,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Chinmay/mlindia | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distil-added-voca
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. -->
# distil-added-voca
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2515
## 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: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 174 | 0.2577 |
| No log | 2.0 | 348 | 0.2488 |
| 0.2546 | 3.0 | 522 | 0.2515 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
ChrisP/xlm-roberta-base-finetuned-marc-en | []
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} | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 3185 with parameters:
```
{'batch_size': 8, '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": 3,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 956,
"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': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
ChrisVCB/DialoGPT-medium-ej | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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},
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}
} | 13 | null | import gradio as gr
def greet(name):
return "Hello " + name + "!!"
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch() |
ChristianOrr/madnet_keras | [
"tensorboard",
"dataset:flyingthings-3d",
"dataset:kitti",
"arxiv:1810.05424",
"vision",
"deep-stereo",
"depth-estimation",
"Tensorflow2",
"Keras",
"license:apache-2.0"
]
| depth-estimation | {
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}
}
} | 0 | null | ---
tags:
- autotrain
- summarization
language:
- en
widget:
- text: "I love AutoTrain 🤗"
co2_eq_emissions:
emissions: 2.970316260186869
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1900964639
- CO2 Emissions (in grams): 2.9703
## Validation Metrics
- Loss: 2.262
- Rouge1: 27.046
- Rouge2: 14.251
- RougeL: 24.913
- RougeLsum: 25.284
- Gen Len: 19.888
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/aalbertini90/autotrain-href-1900964639
``` |
ChukSamuels/DialoGPT-small-Dr.FauciBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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"translation_en_to_ro": {
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}
}
} | 13 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 2 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 2,
"warmup_steps": 1,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Chun/DialoGPT-large-dailydialog | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 6 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: roberta-finetuned-facility
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. -->
# roberta-finetuned-facility
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Chun/DialoGPT-small-dailydialog | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 10 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 2040 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 3,
"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": 2040,
"warmup_steps": 204,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 1024, '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 --> |
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