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huggingtweets/thucydiplease | 814c1f43af471868f6840fb17c1e113ee22c2f6f | 2021-05-23T02:15:35.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/thucydiplease | 17 | null | transformers | 9,000 | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1324921465385279488/JoqDiFxH_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Samantha Pritchard 🤖 AI Bot </div>
<div style="font-size: 15px">@thucydiplease bot</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://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on [@thucydiplease's tweets](https://twitter.com/thucydiplease).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3216 |
| Retweets | 663 |
| Short tweets | 590 |
| Tweets kept | 1963 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/aht8pe1a/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 @thucydiplease's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/k2mweitd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/k2mweitd/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/thucydiplease')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/youronlinedad | 3306b07ac62e97e3c06ed01f6ec02d3b35d7a9b0 | 2021-05-23T05:04:15.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/youronlinedad | 17 | null | transformers | 9,001 | ---
language: en
thumbnail: https://www.huggingtweets.com/youronlinedad/1614100614383/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1184826580910125057/gqE8fCKg_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Internet Dad 🤖 AI Bot </div>
<div style="font-size: 15px">@youronlinedad bot</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://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on [@youronlinedad's tweets](https://twitter.com/youronlinedad).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3201 |
| Retweets | 41 |
| Short tweets | 508 |
| Tweets kept | 2652 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3g7jg14o/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 @youronlinedad's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2t2wy77n) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2t2wy77n/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/youronlinedad')
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)
|
icelab/spacescibert_CR | 5dff26c775e35fe80e50e0d31bb09aff1e5eff95 | 2021-10-25T14:38:27.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | icelab | null | icelab/spacescibert_CR | 17 | null | transformers | 9,002 | ---
widget:
- text: "The CubeSat RF design shall either have one RF inhibit and a RF power output no greater than 1.5W at the transmitter antenna's RF input OR the CubeSat shall have a minimum of two independent RF inhibits (CDS 3.3.9) (ISO 5.5.6)."
---
---
# spacescibert_CR
## Model desciption
This is fine-tuned further SpaceSciBERT model from the SpaceTransformers model family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp. The [fine-tuning](https://github.com/strath-ace/smart-nlp/blob/master/SpaceTransformers/CR/CR_ECSS_dataset.json) dataset is available for download and consists of 874 unique manual annotated ECSS requirements.
The notebookfor fine-tuning can be assesed in Google Colab:
[](https://colab.research.google.com/drive/1EGh9bdxq6RqIzbvKuptAWvmIBG2EQJzJ?usp=sharing)
### BibTeX entry and citation info
```
@ARTICLE{ 9548078,
author={Berquand, Audrey and Darm, Paul and Riccardi, Annalisa},
journal={IEEE Access},
title={SpaceTransformers: Language Modeling for Space Systems},
year={2021},
volume={9},
number={},
pages={133111-133122},
doi={10.1109/ACCESS.2021.3115659} }
``` |
indonesian-nlp/wav2vec2-luganda | 67d044bc4b54f96cb75915dcf6cc7bbcf9cfb288 | 2022-01-19T16:19:45.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"lg",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | indonesian-nlp | null | indonesian-nlp/wav2vec2-luganda | 17 | 1 | transformers | 9,003 | ---
language: lg
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
license: apache-2.0
model-index:
- name: Wav2Vec2 Luganda by Indonesian-NLP
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice lg
type: common_voice
args: lg
metrics:
- name: Test WER
type: wer
value: 7.53
---
# Automatic Speech Recognition for Luganda
This is the model built for the
[Mozilla Luganda Automatic Speech Recognition competition](https://zindi.africa/competitions/mozilla-luganda-automatic-speech-recognition).
It is a fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
model on the [Luganda Common Voice dataset](https://huggingface.co/datasets/common_voice) version 7.0.
We also provide a [live demo](https://huggingface.co/spaces/indonesian-nlp/luganda-asr) to test the model.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "lg", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda")
model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
if "audio" in batch:
speech_array = torch.tensor(batch["audio"]["array"])
else:
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Indonesian test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "lg", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda")
model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda")
model.to("cuda")
chars_to_ignore = [",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", "‘", "’", "’"]
chars_to_ignore_regex = f'[{"".join(chars_to_ignore)}]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
if "audio" in batch:
speech_array = torch.tensor(batch["audio"]["array"])
else:
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
WER without KenLM: 15.38 %
WER With KenLM:
**Test Result**: 7.53 %
## Training
The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO
The script used for training can be found [here](https://github.com/indonesian-nlp/luganda-asr)
|
it5/it5-base-ilgiornale-to-repubblica | db91c86fc720499db2fb99a361076182169d2b96 | 2022-03-09T08:04:46.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"it",
"dataset:gsarti/change_it",
"arxiv:2203.03759",
"transformers",
"italian",
"sequence-to-sequence",
"newspaper",
"ilgiornale",
"repubblica",
"style-transfer",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_compatible"
] | text2text-generation | false | it5 | null | it5/it5-base-ilgiornale-to-repubblica | 17 | null | transformers | 9,004 | ---
language:
- it
license: apache-2.0
datasets:
- gsarti/change_it
tags:
- italian
- sequence-to-sequence
- newspaper
- ilgiornale
- repubblica
- style-transfer
widget:
- text: "WASHINGTON - La Corea del Nord torna dopo nove anni nella blacklist Usa degli Stati considerati sponsor del terrorismo. Come Iran, Siria e Sudan. Lo ha deciso Donald Trump , che ha preferito dare l'annuncio non durante il suo recente viaggio in Asia ma ieri, in una riunione del governo alla Casa Bianca. 'Oggi gli Stati Uniti designeranno la Corea del nord come uno stato sponsor del terrorismo', ha tuonato il tycoon, anticipando che sarà formalizzata oggi dal dipartimento di stato e sarà accompagnata da nuove e più severe sanzioni. 'Il livello più alto' mai imposto a Pyongyang, ha promesso. 'Avrebbe dovuto succedere molto tempo fa', ha aggiunto, scaricando per l'ennesima volta la responsabilità dell'attuale crisi sull'amministrazione Obama. Poi si è scagliato contro un 'regime assassino' che 'deve mettere fine allo sviluppo del suo programma illegale nucleare e balistico'. Per giustificare la svolta, Trump ha accusato Pyongyang non solo di 'minacciare il mondo con una devastazione nucleare' ma anche di aver 'ripetutamente sostenuto atti di terrorismo internazionale', compreso omicidi in suolo straniero. Il riferimento è all' uccisione all'aeroporto della capitale malese di Kim Jong Nam , il fratellastro del leader nordcoreano Kim Jong Un , ma non ci sono altri episodi noti. Tanto che alcuni esperti, come pure dirigenti Usa coperti dall'anonimato, dubitano che Pyongyang risponda ai criteri per una tale designazione. La mossa appare altamente simbolica, dato che la Corea del Nord è già pesantemente sanzionata a livello internazionale. Per il segretario di stato Rex Tillerson è solo l'ultima di una serie di passi per rafforzare la pressione su Pyongyang e costringerla a sedersi ad un tavolo perché gli Usa hanno sempre 'speranza nella diplomazia'. Ma nello stesso tempo è un monito per 'fermare e dissuadere' altri Paesi dal sostenere la Corea del Nord, finita nella blacklist 'anche per l'uso di armi chimiche'. Ma la mossa potrebbe anche essere controproducente, provocando una risposta di Kim o minando gli sforzi per sollecitare Pechino ad una maggiore pressione su Pyongyang. In ogni caso non aiuta il dialogo diretto tra Usa e Corea del Nord, che sembrava essere stato avviato in modo riservato. Come non aiutano gli scambi di insulti fra Trump e Kim. Nord Corea, Trump: 'Cerco di essere amico di Kim, sarebbe una bella cosa per il mondo'. Pyongyang era stata messa nella lista Usa degli Stati sponsor del terrorismo per aver fatto esplodere nel 1987 un volo della Korean Air uccidendo tutti i 115 passeggeri a bordo. Ma l'amministrazione di George W. Bush l'aveva rimossa sperando di far avanzare i negoziati sulla denuclearizzazione della penisola coreana. Il governo giapponese sostiene la decisione degli Stati Uniti di inserire la Corea del Nord nella lista degli stati che sponsorizzano il terrorismo, pur riconoscendo che l'annuncio potrebbe provocare una reazione immediata del regime di Pyongyang. Il premier Shinzo Abe ha accolto con consenso il comunicato Usa e ha detto alla stampa che servirà a incrementare la pressione sulla Corea del Nord. Il ministro della Difesa Itsunori Onodera , pur valutando positivamente la notifica, ha spiegato che si attendono azioni provocatorie dallo stato eremita, ribadendo che è vitale rimanere vigili. Secondo la stampa nipponica Abe aveva richiesto al dipartimento di Stato Usa di mettere la Corea del Nord sulla lista durante l'incontro col presidente Usa Donald Trump a Tokyo a inizio mese. L'ultimo lancio di missile balistico condotto da Pyongyang nell'oceano Pacifico, sorvolando il mare del Giappone, risale allo scorso settembre."
- text: "ROMA - Una nuova droga killer è stata sequestrata per la prima volta in Europa dagli investigatori del Nas. Si tratta di una nuova \"miscela psicoattiva altamente tossica\" per la prima volta individuata da forze di polizia, simile all'eroina sintetica, ma molto più economica e letale. Tanto che i 20 grammi scoperti sarebbero stati sufficienti per fabbricare ben 20.000 dosi e lo stesso contatto attraverso la pelle può provocare intossicazione. Individuata per la prima volta, la nuova droga presenta una struttura simile al farmaco sedativo Fentanyl ma con effetti molto più devastanti per l'organismo. Proveniva dell'estero ed era contenuta in un plico postale indirizzato in una città del centro Italia: è stata intercettata tramite accertamenti sul web grazie a un'operazione di intelligence che ha visto come protagonisti i militari della Sezione operativa centrale del Comando carabinieri per la Tutela della salute (Nas). Economica e letale, secondo gli investigatori \"in confronto l'eroina è quasi 'acqua fresca', anzi, proprio per la sua economicità, in alcuni casi viene venduta dai pusher a giovani conviti di comprare eroina\". La diffusione di nuove droghe sintetiche che continuamente appaiono sui mercati necessita di un'attività investigativa costante e complessa. Si tratta infatti di sostanze dalla struttura molecolare molto simile a quella del Fentanyl ma ogni volta leggermente diversa. Di qui la difficoltà di individuarle e l'importanza del nuovo sequestro. \"La chiamano impropriamente 'eroina sintetica' - spiega il comandante dei Nas, generale Adelmo Lusi - per il tipo di effetto psicotropo simile, ma dal punto di vista della tossicità è molto peggio: con 25 milligrammi di eroina ci si sballa, con 25mg di simil-fentanyl, come quello appena sequestrato, si muore\". Le indagini sono partite da ricoveri per overdose in ospedale, in cui arrivavano ragazzi che non rispondevano al trattamento disintossicante per l'eroina. La nuova sostanza verrà ora segnalata per l'inserimento tra le tabelle ministeriali degli stupefacenti prevista dal Dpr 309/1990."
- text: "Fragile come il burro. Il nostro territorio è precario. Ne sanno qualcosa i comuni che sono stati investititi dal maltempo . Il dissesto idrogeologico imperversa su tutto il territorio. Infatti, oltre 6.600 comuni , pari all’82% del totale, sono in aree ad elevato rischio idrogeologico, pari al 10% della sua superficie. La popolazione potenzialmente esposta è stimata in 5,8 milioni di persone. I dati emergono dalle recenti analisi fatte da Legambiente e Protezione civile, che mettono in evidenza come in 10 anni in Italia sia raddoppiata l’area dei territori colpiti da alluvioni e frane , passando da una media di quattro regioni all’anno a otto regioni. Nella classifica delle regioni a maggior rischio idrogeologico prima è la Calabria con il 100% dei comuni esposti; al 100% ci sono anche la provincia di Trento, il Molise, la Basilicata, l’Umbria, la Valle d’Aosta. Poi Marche, Liguria al 99%; Lazio, Toscana al 98%; Abruzzo (96%), Emilia-Romagna (95%), Campania e Friuli Venezia Giulia al 92%, Piemonte (87%), Sardegna (81%), Puglia (78%), Sicilia (71%), Lombardia (60%), provincia di Bolzano (59%), Veneto (56%). Tra le cause che condizionano ed amplificano il rischio idrogeologico c’è l’azione dell’uomo (abbandono e degrado, cementificazione, consumo di suolo, abusivismo, disboscamento e incendi). Ma anche e soprattutto la mancanza di una seria manutenzione ordinaria e non ad una organica politica di prevenzione."
- text: "Arriva dal Partito nazionalista basco (Pnv) la conferma che i cinque deputati che siedono in parlamento voteranno la sfiducia al governo guidato da Mariano Rajoy. Pochi voti, ma significativi quelli della formazione politica di Aitor Esteban, che interverrà nel pomeriggio. Pur con dimensioni molto ridotte, il partito basco si è trovato a fare da ago della bilancia in aula. E il sostegno alla mozione presentata dai Socialisti potrebbe significare per il primo ministro non trovare quei 176 voti che gli servono per continuare a governare. \" Perché dovrei dimettermi io che per il momento ho la fiducia della Camera e quella che mi è stato data alle urne \", ha detto oggi Rajoy nel suo intervento in aula, mentre procedeva la discussione sulla mozione di sfiducia. Il voto dei baschi ora cambia le carte in tavola e fa crescere ulteriormente la pressione sul premier perché rassegni le sue dimissioni. La sfiducia al premier, o un'eventuale scelta di dimettersi, porterebbe alle estreme conseguenze lo scandalo per corruzione che ha investito il Partito popolare. Ma per ora sembra pensare a tutt'altro. \"Non ha intenzione di dimettersi - ha detto il segretario generale del Partito popolare , María Dolores de Cospedal - Non gioverebbe all'interesse generale o agli interessi del Pp\"."
metrics:
- rouge
- bertscore
- headline-headline-consistency-classifier
- headline-article-consistency-classifier
model-index:
- name: it5-base-ilgiornale-to-repubblica
results:
- task:
type: headline-style-transfer-ilgiornale-to-repubblica
name: "Headline style transfer (Il Giornale to Repubblica)"
dataset:
type: gsarti/change_it
name: "CHANGE-IT"
metrics:
- type: rouge1
value: 0.297
name: "Test Rouge1"
- type: rouge2
value: 0.104
name: "Test Rouge2"
- type: rougeL
value: 0.259
name: "Test RougeL"
- type: bertscore
value: 0.425
name: "Test BERTScore"
args:
- model_type: "dbmdz/bert-base-italian-xxl-uncased"
- lang: "it"
- num_layers: 10
- rescale_with_baseline: True
- baseline_path: "bertscore_baseline_ita.tsv"
- type: headline-headline-consistency-classifier
value: 0.925
name: "Test Headline-Headline Consistency Accuracy"
- type: headline-article-consistency-classifier
value: 0.852
name: "Test Headline-Article Consistency Accuracy"
co2_eq_emissions:
emissions: "17g"
source: "Google Cloud Platform Carbon Footprint"
training_type: "fine-tuning"
geographical_location: "Eemshaven, Netherlands, Europe"
hardware_used: "1 TPU v3-8 VM"
thumbnail: https://gsarti.com/publication/it5/featured.png
---
# IT5 Base for News Headline Style Transfer (Il Giornale to Repubblica) 🗞️➡️🗞️ 🇮🇹
This repository contains the checkpoint for the [IT5 Base](https://huggingface.co/gsarti/it5-base) model fine-tuned on news headline style transfer in the Il Giornale to Repubblica direction on the Italian CHANGE-IT dataset as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io).
A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach.
## Using the model
The model is trained to generate an headline in the style of Repubblica from the full body of an article written in the style of Il Giornale. Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as:
```python
from transformers import pipelines
g2r = pipeline("text2text-generation", model='it5/it5-base-ilgiornale-to-repubblica')
g2r("Arriva dal Partito nazionalista basco (Pnv) la conferma che i cinque deputati che siedono in parlamento voteranno la sfiducia al governo guidato da Mariano Rajoy. Pochi voti, ma significativi quelli della formazione politica di Aitor Esteban, che interverrà nel pomeriggio. Pur con dimensioni molto ridotte, il partito basco si è trovato a fare da ago della bilancia in aula. E il sostegno alla mozione presentata dai Socialisti potrebbe significare per il primo ministro non trovare quei 176 voti che gli servono per continuare a governare. \" Perché dovrei dimettermi io che per il momento ho la fiducia della Camera e quella che mi è stato data alle urne \", ha detto oggi Rajoy nel suo intervento in aula, mentre procedeva la discussione sulla mozione di sfiducia. Il voto dei baschi ora cambia le carte in tavola e fa crescere ulteriormente la pressione sul premier perché rassegni le sue dimissioni. La sfiducia al premier, o un'eventuale scelta di dimettersi, porterebbe alle estreme conseguenze lo scandalo per corruzione che ha investito il Partito popolare. Ma per ora sembra pensare a tutt'altro. \"Non ha intenzione di dimettersi - ha detto il segretario generale del Partito popolare , María Dolores de Cospedal - Non gioverebbe all'interesse generale o agli interessi del Pp\".")
>>> [{"generated_text": "il nazionalista rajoy: 'voteremo la sfiducia'"}]
```
or loaded using autoclasses:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("it5/it5-base-ilgiornale-to-repubblica")
model = AutoModelForSeq2SeqLM.from_pretrained("it5/it5-base-ilgiornale-to-repubblica")
```
If you use this model in your research, please cite our work as:
```bibtex
@article{sarti-nissim-2022-it5,
title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation},
author={Sarti, Gabriele and Nissim, Malvina},
journal={ArXiv preprint 2203.03759},
url={https://arxiv.org/abs/2203.03759},
year={2022},
month={mar}
}
``` |
it5/it5-base-wiki-summarization | d9c0b97204dec4fb765905d1ac50831a8029c557 | 2022-03-09T08:06:40.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"it",
"dataset:wits",
"arxiv:2203.03759",
"transformers",
"italian",
"sequence-to-sequence",
"wikipedia",
"summarization",
"wits",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_compatible"
] | summarization | false | it5 | null | it5/it5-base-wiki-summarization | 17 | null | transformers | 9,005 | ---
language:
- it
license: apache-2.0
datasets:
- wits
tags:
- italian
- sequence-to-sequence
- wikipedia
- summarization
- wits
widget:
- text: "La 5ª Commissione ha competenza per i disegni di legge riguardanti le specifiche materie del bilancio, del personale e dei servizi del Ministero dell'economia, nonché per i disegni di legge riguardanti la materia finanziaria. La Commissione è composta da 26 senatori (di cui 2 segretari, 2 vicepresidenti di cui 1 componente esterno, e un presidente) scelti in modo omogeneo tra i componenti di quel ramo del Parlamento, in modo da rispecchiarne le forze politiche presenti. Essi sono scelti dai gruppi parlamentari (e non dal Presidente, come invece accade per l'organismo della Giunta parlamentare): per la nomina dei membri ciascun Gruppo, entro cinque giorni dalla propria costituzione, procede, dandone comunicazione alla Presidenza del Senato, alla designazione dei propri rappresentanti nelle singole Commissioni permanenti. Ogni senatore chiamato a far parte del governo o eletto presidente della Commissione è, per la durata della carica, sostituito dal suo gruppo nella Commissione con un altro senatore, che continuerà ad appartenere anche alla Commissione di provenienza. Tranne in rari casi nessun Senatore può essere assegnato a più di una Commissione permanente. Le Commissioni permanenti sono rinnovate dopo il primo biennio della legislatura ed i loro componenti possono essere confermati."
- text: "Interni della chiesa Si pensa che già ai tempi di Gediminas vi fosse una piccola chiesa, probabilmente in legno. Nel 1408 circa Vitoldo costruì la chiesa dello Spirito Santo che andò in seguito ampliata. Nel 1501 Alessandro Jagellone lo donò al monastero domenicano, il più antico della Lituania, che nel 1679-88 fu ampliato e ricostruito. Di quel periodo sopravvivono le mura della chiesa, mentre l'arredamento interno fu realizzato nel 1749-1770 e la cupola affrontò dei lavori di restauro nel 1752-1760. Nel 1844 le autorità zariste chiusero il monastero e la chiesa divenne parrocchiale. Oggi serve la comunità polacca di Vilnius. Su via Šv. Ignoto fu fondato un monastero domenicano nel 1501. Come molti altri edifici, questo monastero fu convertito in una prigione dalle autorità zariste nel 1807. Costituì un luogo di prigionia per molti patrioti lituani, nello specifico i Filareti, i quali parteciparono alle rivolte del 1831 e del 1863. Organo La chiesa si trova lateralmente rispetto alla strada e non ha una facciata principale ben disegnata. L'altezza, inclusa la cupola, è di 51 m. La parte inferiore della facciata (con piccole torri gemelle) è ricoperta da edifici conventuali e l'esterno presenta caratteristiche architettoniche tipiche del tardo barocco. Celebre per i fantasiosi ornamenti rococò, l'interno della chiesa è tra i più celebri della Lituania per via dei cartigli con vari stemmi e affreschi lungo la navata: vi sono 16 altari nella chiesa. Gli altari e il pulpito sono assai decorati con sculture e ornamenti rotondi e in rilievo. Tra gli affreschi barocchi, si pensi alla composizione multi-figurale intitolata ''Apoteosi dello Spirito Santo'' (neobarocco, XIX secolo) nella cupola, 45 dipinti nella chiesa (tra cui un'immagine di Santa Barbara con un'ambientazione del XVII o XVIII secolo, una di Santa Caterina da Siena in stile rococò di Szymon Czechowicz, un ritratto di Alessandro Jagellone di un artista sconosciuto della seconda metà del XVIII secolo). Un ingresso sotto l'altare conduce alle grandi volte, labirintiche, con molte stanze e cripte: i sotterranei ospitano i resti di centinaia di residenti di Vilnius, alcuni dei quali mummificatisi naturalmente, e sono circondati da leggende metropolitane. Sebbene l'esistenza dei sotterranei fosse nota, i primi sforzi per esplorare e mappare le cripte furono abbandonate nonostante lo sforzo degli studenti dell'Università di Vilnius negli anni '30. Tuttavia, questi ultimi non avevano osservato le corrette procedure archeologiche e causarono infatti molti danni: il modus operandi prevedeva lo smistamento delle ossa ponendo tutti i teschi sugli scaffali e rimuovendoli le tombe. Da allora, i resti sono stati spostati molte volte lasciandoli in uno stato casuale e disorganizzato. Stando alle leggende che aleggiano sul luogo, i resti sarebbero di soldati francesi recatisi in città nel corso della campagna di Russia del 1812 avviata da Napoleone Bonaparte, di vittime dell'Inquisizione o della peste nera. Più romantiche risultano le affermazioni di chi sostiene che i corridoi sotterranei facevano parte di una rete di passaggi più ampia che consentiva agli amanti leggendari Barbara Radziwiłł e Sigismondo II Augusto di incontrarsi in segreto. Nel 2011, gli antropologi dell'Università di Vilnius, guidati da Rimantas Jankauskas, avviarono uno studio sui corpi mummificati, stimando settimane dopo che le volte conservassero i resti di circa 600 persone, tra cui molte donne e bambini dalla metà del XVIII secolo all'inizio del XIX secolo. Il team ha selezionato i cadaveri meglio conservati e ha eseguito la loro tomografia. I risultati mostrano che molte persone erano in sovrappeso e avevano l'alluce valgo, il che ha portato alla conclusione che si trattava di alti borghesi o comunque di cittadini abbienti. "
- text: "Le dimensioni dell'isola sono di 8 km di lunghezza e di 3,2 km di larghezza. Si trova a 1,6 km a sud-est dell'isola di Renaud, dalla quale è separata dal passaggio Rodman. La sua altezza è di 100 m. Fu scoperta dall'esploratore e baleniere britannico John Biscoe nel 1832 e venne mappata durante una spedizione antartica francese realizzata nel primo decennio del XX secolo. Al comando della spedizione era Jean-Baptiste Charcot e il nome fu scelto per onorare l'esploratore e geografo francese Charles Rabot. === Rivendicazioni territoriali === * Secondo l'Argentina appartiene al dipartimento dell'Antartide Argentina nella provincia della Terra del Fuoco. * Secondo il Cile appartiene al comune antartico della provincia cilena antartica nella regione di Magallanes e dell'Antartico cileno. * Secondo il Regno Unito fa parte del territorio antartico britannico. Per il Trattato Antartico tali rivendicazioni sono sospese. Sull'isola è presente il rifugio Guillochon, sito storico antartico. "
- text: "Vanni ha la sua prima mostra personale nel 1948, alla Galleria Margherita di Roma. Nel 1949 vince una borsa di studio che lo porterà a studiare ad Amsterdam sotto la guida del pittore neoplastico Friedrich Vordemberge-Gildewart. Nel 1952 vince una Fulbright Scholarship che lo porterà a studiare in America, alla Yale University, sotto la guida di Josef Albers. Dal 1953 al 1960 si stabilisce a Parigi, dove illustra alcuni libri per bambini che in seguito vinceranno il premio del Club des Editeurs. Nel 1954 lavora come consulente del colore per il documentario su Picasso di Luciano Emmer, e nel 1955 comincia la sua lunga collaborazione con la Galleria Schneider, affiancando artisti come Corrado Cagli. Dal 1969 al 1974 lavora su dei bassorilievi in vetro resina sui quali vengono proiettati dei film astratti da lui creati, per creare dei quadri che si trasformino continuamente nel tempo. Nel 1979 lascia Roma per stabilirsi a New York, dove alla carriera di pittore affiancherà quella di professore per la prestigiosa Cooper Union School of Art, dove insegnerà ininterrottamente dal 1984 al 2014. L'opera pittorica di Vanni è segnata da una visione estremamente personale, lontana dalle correnti e dai movimenti che hanno caratterizzato la seconda metà del XX secolo. Memore delle lunghe conversazioni avute da Vanni nella sua primissima gioventù, con il filosofo e pittore futurista Alberto Bragaglia, le sue opere sono contrassegnate da un “eclettismo” formale programmatico, alla base del quale resta costante una conoscenza profonda delle molteplici tecniche artistiche utilizzate (tra cui il mosaico, l’affresco e la tempera ad uovo). Pur esprimendosi per lo più in cicli di opere dove l’astrazione formale è la principale componente figurativa, sono da sottolineare alcune opere dove Vanni ha dato prova di una importante padronanza dell’arte figurativa. Importanti e numerose sono le sue realizzazioni anche nel campo dell’illustrazione. Sue sono le illustrazioni per la novella ''Agostino'' di Alberto Moravia, per il libro ''Love'' di Lowell A. Siff e delle ''Contes de Cristal'' di Alice Coléno. Ha tenuto mostre personali in Italia e all’estero ed esposto in mostre collettive di rappresentanza italiana nei musei e nelle gallerie di ogni parte del mondo. "
metrics:
- rouge
- bertscore
model-index:
- name: it5-base-wiki-summarization
results:
- task:
type: wiki-summarization
name: "Wikipedia Summarization"
dataset:
type: wits
name: "WITS"
metrics:
- type: rouge1
value: 0.369
name: "Test Rouge1"
- type: rouge2
value: 0.217
name: "Test Rouge2"
- type: rougeL
value: 0.333
name: "Test RougeL"
- type: bertscore
value: 0.530
name: "Test BERTScore"
args:
- model_type: "dbmdz/bert-base-italian-xxl-uncased"
- lang: "it"
- num_layers: 10
- rescale_with_baseline: True
- baseline_path: "bertscore_baseline_ita.tsv"
co2_eq_emissions:
emissions: "17g"
source: "Google Cloud Platform Carbon Footprint"
training_type: "fine-tuning"
geographical_location: "Eemshaven, Netherlands, Europe"
hardware_used: "1 TPU v3-8 VM"
thumbnail: https://gsarti.com/publication/it5/featured.png
---
# IT5 Base for Wikipedia Summarization 📑 🇮🇹
This repository contains the checkpoint for the [IT5 Base](https://huggingface.co/gsarti/it5-base) model fine-tuned on Wikipedia summarization on the [WITS](https://www.semanticscholar.org/paper/WITS%3A-Wikipedia-for-Italian-Text-Summarization-Casola-Lavelli/ad6c83122e721c7c0db4a40727dac3b4762cd2b1) dataset as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io).
A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach.
## Using the model
Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as:
```python
from transformers import pipelines
hg = pipeline("text2text-generation", model='it5/it5-base-wiki-summarization')
hg("Le dimensioni dell'isola sono di 8 km di lunghezza e di 3,2 km di larghezza. Si trova a 1,6 km a sud-est dell'isola di Renaud, dalla quale è separata dal passaggio Rodman. La sua altezza è di 100 m. Fu scoperta dall'esploratore e baleniere britannico John Biscoe nel 1832 e venne mappata durante una spedizione antartica francese realizzata nel primo decennio del XX secolo. Al comando della spedizione era Jean-Baptiste Charcot e il nome fu scelto per onorare l'esploratore e geografo francese Charles Rabot. === Rivendicazioni territoriali === * Secondo l'Argentina appartiene al dipartimento dell'Antartide Argentina nella provincia della Terra del Fuoco. * Secondo il Cile appartiene al comune antartico della provincia cilena antartica nella regione di Magallanes e dell'Antartico cileno. * Secondo il Regno Unito fa parte del territorio antartico britannico. Per il Trattato Antartico tali rivendicazioni sono sospese. Sull'isola è presente il rifugio Guillochon, sito storico antartico. "
- text: "Vanni ha la sua prima mostra personale nel 1948, alla Galleria Margherita di Roma. Nel 1949 vince una borsa di studio che lo porterà a studiare ad Amsterdam sotto la guida del pittore neoplastico Friedrich Vordemberge-Gildewart. Nel 1952 vince una Fulbright Scholarship che lo porterà a studiare in America, alla Yale University, sotto la guida di Josef Albers. Dal 1953 al 1960 si stabilisce a Parigi, dove illustra alcuni libri per bambini che in seguito vinceranno il premio del Club des Editeurs. Nel 1954 lavora come consulente del colore per il documentario su Picasso di Luciano Emmer, e nel 1955 comincia la sua lunga collaborazione con la Galleria Schneider, affiancando artisti come Corrado Cagli. Dal 1969 al 1974 lavora su dei bassorilievi in vetro resina sui quali vengono proiettati dei film astratti da lui creati, per creare dei quadri che si trasformino continuamente nel tempo. Nel 1979 lascia Roma per stabilirsi a New York, dove alla carriera di pittore affiancherà quella di professore per la prestigiosa Cooper Union School of Art, dove insegnerà ininterrottamente dal 1984 al 2014. L'opera pittorica di Vanni è segnata da una visione estremamente personale, lontana dalle correnti e dai movimenti che hanno caratterizzato la seconda metà del XX secolo. Memore delle lunghe conversazioni avute da Vanni nella sua primissima gioventù, con il filosofo e pittore futurista Alberto Bragaglia, le sue opere sono contrassegnate da un “eclettismo” formale programmatico, alla base del quale resta costante una conoscenza profonda delle molteplici tecniche artistiche utilizzate (tra cui il mosaico, l’affresco e la tempera ad uovo). Pur esprimendosi per lo più in cicli di opere dove l’astrazione formale è la principale componente figurativa, sono da sottolineare alcune opere dove Vanni ha dato prova di una importante padronanza dell’arte figurativa. Importanti e numerose sono le sue realizzazioni anche nel campo dell’illustrazione. Sue sono le illustrazioni per la novella ''Agostino'' di Alberto Moravia, per il libro ''Love'' di Lowell A. Siff e delle ''Contes de Cristal'' di Alice Coléno. Ha tenuto mostre personali in Italia e all’estero ed esposto in mostre collettive di rappresentanza italiana nei musei e nelle gallerie di ogni parte del mondo.")
>>> [{"generated_text": "L' '''isola di Rabot''' si trova in prossimità dell'isola di Renaud, a sud dell'Argentina."}]
```
or loaded using autoclasses:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("it5/it5-base-wiki-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("it5/it5-base-wiki-summarization")
```
If you use this model in your research, please cite our work as:
```bibtex
@article{sarti-nissim-2022-it5,
title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation},
author={Sarti, Gabriele and Nissim, Malvina},
journal={ArXiv preprint 2203.03759},
url={https://arxiv.org/abs/2203.03759},
year={2022},
month={mar}
}
``` |
julien-c/EsperBERTo-small-pos | 1183bc1ab394cc09d9c631c07b076cdcedd77954 | 2021-05-20T17:28:42.000Z | [
"pytorch",
"jax",
"roberta",
"token-classification",
"eo",
"transformers",
"autotrain_compatible"
] | token-classification | false | julien-c | null | julien-c/EsperBERTo-small-pos | 17 | 1 | transformers | 9,006 | ---
language: eo
thumbnail: https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png
widget:
- text: "Mi estas viro kej estas tago varma."
---
# EsperBERTo: RoBERTa-like Language model trained on Esperanto
**Companion model to blog post https://huggingface.co/blog/how-to-train** 🔥
## Training Details
- current checkpoint: 566000
- machine name: `galinette`
## Example pipeline
```python
from transformers import TokenClassificationPipeline, pipeline
MODEL_PATH = "./models/EsperBERTo-small-pos/"
nlp = pipeline(
"ner",
model=MODEL_PATH,
tokenizer=MODEL_PATH,
)
# or instantiate a TokenClassificationPipeline directly.
nlp("Mi estas viro kej estas tago varma.")
# {'entity': 'PRON', 'score': 0.9979867339134216, 'word': ' Mi'}
# {'entity': 'VERB', 'score': 0.9683094620704651, 'word': ' estas'}
# {'entity': 'VERB', 'score': 0.9797462821006775, 'word': ' estas'}
# {'entity': 'NOUN', 'score': 0.8509314060211182, 'word': ' tago'}
# {'entity': 'ADJ', 'score': 0.9996201395988464, 'word': ' varma'}
``` |
juliensimon/autonlp-imdb-demo-hf-16622775 | f679643e1e113864071f50a08815be4652aded48 | 2021-10-11T12:46:02.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:juliensimon/autonlp-data-imdb-demo-hf",
"transformers",
"autonlp"
] | text-classification | false | juliensimon | null | juliensimon/autonlp-imdb-demo-hf-16622775 | 17 | 1 | transformers | 9,007 | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- juliensimon/autonlp-data-imdb-demo-hf
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 16622775
## Validation Metrics
- Loss: 0.18653589487075806
- Accuracy: 0.9408
- Precision: 0.9537643207855974
- Recall: 0.9272076372315036
- AUC: 0.985847396174344
- F1: 0.9402985074626865
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/juliensimon/autonlp-imdb-demo-hf-16622775
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("juliensimon/autonlp-imdb-demo-hf-16622775", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("juliensimon/autonlp-imdb-demo-hf-16622775", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
junnyu/roformer_small_discriminator | 411682a5b9cb673344ebd2ebf6482612c1c6006f | 2021-09-22T08:54:23.000Z | [
"pytorch",
"roformer",
"feature-extraction",
"en",
"dataset:openwebtext",
"transformers",
"electra",
"rotary position embedding",
"license:mit"
] | feature-extraction | false | junnyu | null | junnyu/roformer_small_discriminator | 17 | null | transformers | 9,008 | ---
language: en
thumbnail: https://github.com/junnyu
tags:
- pytorch
- electra
- roformer
- rotary position embedding
license: mit
datasets:
- openwebtext
---
# 一、 个人在openwebtext数据集上添加rotary-position-embedding，训练得到的electra-small模型
# 二、 复现结果(dev dataset)
|Model|CoLA|SST|MRPC|STS|QQP|MNLI|QNLI|RTE|Avg.|
|---|---|---|---|---|---|---|---|---|---|
|ELECTRA-Small-OWT(original)|56.8|88.3|87.4|86.8|88.3|78.9|87.9|68.5|80.36|
|**ELECTRA-RoFormer-Small-OWT (this)**|55.76|90.45|87.3|86.64|89.61|81.17|88.85|62.71|80.31|
# 三、 训练细节
- 数据集 openwebtext
- 训练batch_size 256
- 学习率lr 5e-4
- 最大句子长度max_seqlen 128
- 训练total step 50W
- GPU RTX3090
- 训练时间总共耗费55h
# 四、wandb日志
- [**预训练日志**](https://wandb.ai/junyu/electra_rotary_small_pretrain?workspace=user-junyu)
- [**GLUE微调日志**](https://wandb.ai/junyu/electra_rotary_glue_100?workspace=user-junyu)
# 五、 使用
```python
import torch
from transformers import ElectraTokenizer,RoFormerModel
tokenizer = ElectraTokenizer.from_pretrained("junnyu/roformer_small_discriminator")
model = RoFormerModel.from_pretrained("junnyu/roformer_small_discriminator")
inputs = tokenizer("Beijing is the capital of China.", return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
print(outputs[0].shape)
``` |
kaesve/BioBERT_patent_reference_extraction | a1e26ee5926ce7bf00ccc2a08d11d099cf24da91 | 2021-05-19T20:58:49.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"arxiv:2101.01039",
"transformers",
"autotrain_compatible"
] | fill-mask | false | kaesve | null | kaesve/BioBERT_patent_reference_extraction | 17 | null | transformers | 9,009 | # Reference extraction in patents
This repository contains a finetuned BioBERT model that can extract references to scientific literature from patents.
See https://github.com/kaesve/patent-citation-extraction and https://arxiv.org/abs/2101.01039 for more information.
|
kuppuluri/telugu_bertu_ner | c1649a30768be0256c2d4375cc45baf64f1c1199 | 2021-12-02T18:15:04.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | kuppuluri | null | kuppuluri/telugu_bertu_ner | 17 | null | transformers | 9,010 | # Named Entity Recognition Model for Telugu
#### How to use
Use the below script from your python terminal as the web interface for inference has few encoding issues for Telugu
PS: If you find my model useful, I would appreciate a note from you as it would encourage me to continue improving it and also add new models.
```python
from simpletransformers.ner import NERModel
model = NERModel('bert',
'kuppuluri/telugu_bertu_ner',
labels=[
'B-PERSON', 'I-ORG', 'B-ORG', 'I-LOC', 'B-MISC',
'I-MISC', 'I-PERSON', 'B-LOC', 'O'
],
use_cuda=False,
args={"use_multiprocessing": False})
text = "విరాట్ కోహ్లీ కూడా అదే నిర్లక్ష్యాన్ని ప్రదర్శించి కేవలం ఒక పరుగుకే రనౌటై పెవిలియన్ చేరాడు ."
results = model.predict([text])
```
## Training data
Training data is from https://github.com/anikethjr/NER_Telugu
## Eval results
On the test set my results were
eval_loss = 0.0004407190410447974
f1_score = 0.999519076627124
precision = 0.9994389677005691
recall = 0.9995991983967936
|
ltrctelugu/gpt2_ltrc_telugu | c31edbe619c6cce20bfecaab8b843095c0dd2738 | 2021-05-23T08:35:13.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | ltrctelugu | null | ltrctelugu/gpt2_ltrc_telugu | 17 | null | transformers | 9,011 | Entry not found |
ltrctelugu/ltrc-distilbert | 0fc61ff343b7d8a916b8245293148084c66f25f0 | 2021-11-22T11:34:05.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | ltrctelugu | null | ltrctelugu/ltrc-distilbert | 17 | null | transformers | 9,012 | hello
|
m3hrdadfi/albert-fa-base-v2-ner-peyma | e6f7d8a4e274f0a26de0e2c704c38ad2d7145c73 | 2020-12-26T08:36:20.000Z | [
"pytorch",
"tf",
"albert",
"token-classification",
"fa",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | m3hrdadfi | null | m3hrdadfi/albert-fa-base-v2-ner-peyma | 17 | 1 | transformers | 9,013 | ---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 3.9M documents, 73M sentences, and 1.3B words, like the way we did for ParsBERT.
Please follow the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo for the latest information about previous and current models.
## Persian NER [ARMAN, PEYMA]
This task aims to extract named entities in the text, such as names and label with appropriate `NER` classes such as locations, organizations, etc. The datasets used for this task contain sentences that are marked with `IOB` format. In this format, tokens that are not part of an entity are tagged as `”O”` the `”B”`tag corresponds to the first word of an object, and the `”I”` tag corresponds to the rest of the terms of the same entity. Both `”B”` and `”I”` tags are followed by a hyphen (or underscore), followed by the entity category. Therefore, the NER task is a multi-class token classification problem that labels the tokens upon being fed a raw text. There are two primary datasets used in Persian NER, `ARMAN`, and `PEYMA`.
### PEYMA
PEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.
1. Organization
2. Money
3. Location
4. Date
5. Time
6. Person
7. Percent
| Label | # |
|:------------:|:-----:|
| Organization | 16964 |
| Money | 2037 |
| Location | 8782 |
| Date | 4259 |
| Time | 732 |
| Person | 7675 |
| Percent | 699 |
**Download**
You can download the dataset from [here](http://nsurl.org/tasks/task-7-named-entity-recognition-ner-for-farsi/)
## Results
The following table summarizes the F1 score obtained as compared to other models and architectures.
| Dataset | ALBERT-fa-base-v2 | ParsBERT-v1 | mBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF |
|:-------:|:-----------------:|:-----------:|:-----:|:----------:|:------------:|:--------:|:--------------:|:----------:|
| PEYMA | 88.99 | 93.10 | 86.64 | - | 90.59 | - | 84.00 | - |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@misc{ALBERTPersian,
author = {Mehrdad Farahani},
title = {ALBERT-Persian: A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/m3hrdadfi/albert-persian}},
}
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo. |
madlag/bert-base-uncased-squadv1-x2.01-f89.2-d30-hybrid-rewind-opt-v1 | 151170c410fad18bd5890fa53cba8a3c06d56805 | 2021-06-16T15:02:14.000Z | [
"pytorch",
"tf",
"bert",
"question-answering",
"en",
"dataset:squad",
"transformers",
"license:mit",
"autotrain_compatible"
] | question-answering | false | madlag | null | madlag/bert-base-uncased-squadv1-x2.01-f89.2-d30-hybrid-rewind-opt-v1 | 17 | null | transformers | 9,014 | ---
language: en
thumbnail:
license: mit
tags:
- question-answering
-
-
datasets:
- squad
metrics:
- squad
widget:
- text: "Where is the Eiffel Tower located?"
context: "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eiffel, whose company designed and built the tower."
- text: "Who is Frederic Chopin?"
context: "Frédéric François Chopin, born Fryderyk Franciszek Chopin (1 March 1810 – 17 October 1849), was a Polish composer and virtuoso pianist of the Romantic era who wrote primarily for solo piano."
---
## BERT-base uncased model fine-tuned on SQuAD v1
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 30.0%** of the original weights.
This model **CANNOT be used without using nn_pruning `optimize_model`** function, as it uses NoNorms instead of LayerNorms and this is not currently supported by the Transformers library.
It uses ReLUs instead of GeLUs as in the initial BERT network, to speedup inference.
This does not need special handling, as it is supported by the Transformers library, and flagged in the model config by the ```"hidden_act": "relu"``` entry.
The model contains **45.0%** of the original weights **overall** (the embeddings account for a significant part of the model, and they are not pruned by this method).
With a simple resizing of the linear matrices it ran **2.01x as fast as bert-base-uncased** on the evaluation.
This is possible because the pruning method lead to structured matrices: to visualize them, hover below on the plot to see the non-zero/zero parts of each matrix.
<div class="graph"><script src="/madlag/bert-base-uncased-squadv1-x2.01-f89.2-d30-hybrid-rewind-opt-v1/raw/main/model_card/density_info.js" id="c3b978cc-6d18-4fd0-a24b-e4369569d64d"></script></div>
In terms of accuracy, its **F1 is 89.19**, compared with 88.5 for bert-base-uncased, a **F1 gain of 0.69**.
## Fine-Pruning details
This model was fine-tuned from the HuggingFace [model](https://huggingface.co/bert-base-uncased) checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer), and distilled from the model [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad)
This model is case-insensitive: it does not make a difference between english and English.
A side-effect of the block pruning is that some of the attention heads are completely removed: 55 heads were removed on a total of 144 (38.2%).
Here is a detailed view on how the remaining heads are distributed in the network after pruning.
<div class="graph"><script src="/madlag/bert-base-uncased-squadv1-x2.01-f89.2-d30-hybrid-rewind-opt-v1/raw/main/model_card/pruning_info.js" id="7de38b6d-774c-4313-a5a4-8e32f554d9ec"></script></div>
## Details of the SQuAD1.1 dataset
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| SQuAD1.1 | train | 90.6K |
| SQuAD1.1 | eval | 11.1k |
### Fine-tuning
- Python: `3.8.5`
- Machine specs:
```CPU: Intel(R) Core(TM) i7-6700K CPU
Memory: 64 GiB
GPUs: 1 GeForce GTX 3090, with 24GiB memory
GPU driver: 455.23.05, CUDA: 11.1
```
### Results
**Pytorch model file size**: `374MB` (original BERT: `420MB`)
| Metric | # Value | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| Variation |
| ------ | --------- | --------- | --------- |
| **EM** | **82.21** | **80.8** | **+1.41**|
| **F1** | **89.19** | **88.5** | **+0.69**|
## Example Usage
Install nn_pruning: it contains the optimization script, which just pack the linear layers into smaller ones by removing empty rows/columns.
`pip install nn_pruning`
Then you can use the `transformers library` almost as usual: you just have to call `optimize_model` when the pipeline has loaded.
```python
from transformers import pipeline
from nn_pruning.inference_model_patcher import optimize_model
qa_pipeline = pipeline(
"question-answering",
model="madlag/bert-base-uncased-squadv1-x2.01-f89.2-d30-hybrid-rewind-opt-v1",
tokenizer="madlag/bert-base-uncased-squadv1-x2.01-f89.2-d30-hybrid-rewind-opt-v1"
)
print("bert-base-uncased parameters: 200.0M")
print(f"Parameters count (includes only head pruning, not feed forward pruning)={int(qa_pipeline.model.num_parameters() / 1E6)}M")
qa_pipeline.model = optimize_model(qa_pipeline.model, "dense")
print(f"Parameters count after complete optimization={int(qa_pipeline.model.num_parameters() / 1E6)}M")
predictions = qa_pipeline({
'context': "Frédéric François Chopin, born Fryderyk Franciszek Chopin (1 March 1810 – 17 October 1849), was a Polish composer and virtuoso pianist of the Romantic era who wrote primarily for solo piano.",
'question': "Who is Frederic Chopin?",
})
print("Predictions", predictions)
``` |
maroo93/squad1.1 | 250b75f3eed58a84c3094d8deb08270287ed5bf2 | 2021-05-19T23:07:37.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | maroo93 | null | maroo93/squad1.1 | 17 | null | transformers | 9,015 | Entry not found |
mrm8488/bert-small-finetuned-typo-detection | c78290d7b75061bf5bedab66e589def2cec7372e | 2021-05-25T20:20:35.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"en",
"transformers",
"autotrain_compatible"
] | token-classification | false | mrm8488 | null | mrm8488/bert-small-finetuned-typo-detection | 17 | null | transformers | 9,016 | ---
language: en
thumbnail:
widget:
- text: "here there is an error in coment"
---
# BERT SMALL + Typo Detection ✍❌✍✔
[BERT SMALL](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) fine-tuned on [GitHub Typo Corpus](https://github.com/mhagiwara/github-typo-corpus) for **typo detection** (using *NER* style)
## Details of the downstream task (Typo detection as NER)
- Dataset: [GitHub Typo Corpus](https://github.com/mhagiwara/github-typo-corpus) 📚
- [Fine-tune script on NER dataset provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner_old.py) 🏋️♂️
## Metrics on test set 📋
| Metric | # score |
| :-------: | :-------: |
| F1 | **89.12** |
| Precision | **93.82** |
| Recall | **84.87** |
## Model in action 🔨
Fast usage with **pipelines** 🧪
```python
from transformers import pipeline
typo_checker = pipeline(
"ner",
model="mrm8488/bert-small-finetuned-typo-detection",
tokenizer="mrm8488/bert-small-finetuned-typo-detection"
)
result = typo_checker("here there is an error in coment")
result[1:-1]
# Output:
[{'entity': 'ok', 'score': 0.9021041989326477, 'word': 'here'},
{'entity': 'ok', 'score': 0.7975626587867737, 'word': 'there'},
{'entity': 'ok', 'score': 0.8596242070198059, 'word': 'is'},
{'entity': 'ok', 'score': 0.7071516513824463, 'word': 'an'},
{'entity': 'ok', 'score': 0.943381130695343, 'word': 'error'},
{'entity': 'ok', 'score': 0.8047608733177185, 'word': 'in'},
{'entity': 'ok', 'score': 0.8240702152252197, 'word': 'come'},
{'entity': 'typo', 'score': 0.5004884004592896, 'word': '##nt'}]
```
It works🎉! we typed ```coment``` instead of ```comment```
Let's try with another example
```python
result = typo_checker("Adddd validation midelware")
result[1:-1]
# Output:
[{'entity': 'ok', 'score': 0.7128152847290039, 'word': 'add'},
{'entity': 'typo', 'score': 0.5388424396514893, 'word': '##dd'},
{'entity': 'ok', 'score': 0.94792640209198, 'word': 'validation'},
{'entity': 'typo', 'score': 0.5839331746101379, 'word': 'mid'},
{'entity': 'ok', 'score': 0.5195121765136719, 'word': '##el'},
{'entity': 'ok', 'score': 0.7222476601600647, 'word': '##ware'}]
```
Yeah! We typed wrong ```Add and middleware```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
mrm8488/electricidad-base-discriminator | 1353d86e74c5ae322590dcda6e216259b1f72b67 | 2022-03-30T20:42:47.000Z | [
"pytorch",
"electra",
"pretraining",
"es",
"dataset:-large_spanish_corpus",
"transformers",
"Spanish",
"Electra"
] | null | false | mrm8488 | null | mrm8488/electricidad-base-discriminator | 17 | 2 | transformers | 9,017 | ---
language: es
thumbnail: https://i.imgur.com/uxAvBfh.png
tags:
- Spanish
- Electra
datasets:
-large_spanish_corpus
---
## ELECTRICIDAD: The Spanish Electra [Imgur](https://imgur.com/uxAvBfh)
**Electricidad-base-discriminator** (uncased) is a ```base``` Electra like model (discriminator in this case) trained on a [Large Spanish Corpus](https://github.com/josecannete/spanish-corpora) (aka BETO's corpus)
As mentioned in the original [paper](https://openreview.net/pdf?id=r1xMH1BtvB):
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset.
For a detailed description and experimental results, please refer the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB).
## Model details ⚙
|Name| # Value|
|-----|--------|
|Layers| 12 |
|Hidden | 768 |
|Params| 110M |
## Evaluation metrics (for discriminator) 🧾
|Metric | # Score |
|-------|---------|
|Accuracy| 0.985|
|Precision| 0.726|
|AUC | 0.922|
## Fast example of usage 🚀
```python
from transformers import ElectraForPreTraining, ElectraTokenizerFast
import torch
discriminator = ElectraForPreTraining.from_pretrained("mrm8488/electricidad-base-discriminator")
tokenizer = ElectraTokenizerFast.from_pretrained("mrm8488/electricidad-base-discriminator")
sentence = "El rápido zorro marrón salta sobre el perro perezoso"
fake_sentence = "El rápido zorro marrón amar sobre el perro perezoso"
fake_tokens = tokenizer.tokenize(fake_sentence)
fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
discriminator_outputs = discriminator(fake_inputs)
predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
[print("%7s" % token, end="") for token in fake_tokens]
[print("%7s" % prediction, end="") for prediction in predictions.tolist()]
# Output:
'''
el rapido zorro marro ##n amar sobre el perro pere ##zoso 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0[None, None, None, None, None, None, None, None, None, None, None, None, None
'''
```
As you can see there are **1s** in the places where the model detected a fake token. So, it works! 🎉
### Some models fine-tuned on a downstream task 🛠️
[Question Answering](https://huggingface.co/mrm8488/electricidad-base-finetuned-squadv1-es)
[POS](https://huggingface.co/mrm8488/electricidad-base-finetuned-pos)
[NER](https://huggingface.co/mrm8488/electricidad-base-finetuned-ner)
### Spanish LM model comparison 📊
| Dataset | Metric | RoBERTa-b | RoBERTa-l | BETO | mBERT | BERTIN | Electricidad-b |
|-------------|----------|-----------|-----------|--------|--------|--------|---------|
| UD-POS | F1 | 0.9907 | 0.9901 | 0.9900 | 0.9886 | 0.9904 | 0.9818 |
| Conll-NER | F1 | 0.8851 | 0.8772 | 0.8759 | 0.8691 | 0.8627 | 0.7954 |
| Capitel-POS | F1 | 0.9846 | 0.9851 | 0.9836 | 0.9839 | 0.9826 | 0.9816 |
| Capitel-NER | F1 | 0.8959 | 0.8998 | 0.8771 | 0.8810 | 0.8741 | 0.8035 |
| STS | Combined | 0.8423 | 0.8420 | 0.8216 | 0.8249 | 0.7822 | 0.8065 |
| MLDoc | Accuracy | 0.9595 | 0.9600 | 0.9650 | 0.9560 | 0.9673 | 0.9490 |
| PAWS-X | F1 | 0.9035 | 0.9000 | 0.8915 | 0.9020 | 0.8820 | **0.9045** |
| XNLI | Accuracy | 0.8016 | 0.7958 | 0.8130 | 0.7876 | 0.7864 | 0.7878 |
## Acknowledgments
I thank [🤗/transformers team](https://github.com/huggingface/transformers) for allowing me to train the model (specially to [Julien Chaumond](https://twitter.com/julien_c)).
## Citation
If you want to cite this model you can use this:
```bibtex
@misc{mromero2020electricidad-base-discriminator,
title={Spanish Electra by Manuel Romero},
author={Romero, Manuel},
publisher={Hugging Face},
journal={Hugging Face Hub},
howpublished={\url{https://huggingface.co/mrm8488/electricidad-base-discriminator/}},
year={2020}
}
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
mrm8488/longformer-base-4096-spanish | 38c75a848ba74f488916841566f57f5ce2c57b60 | 2022-03-30T20:36:36.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"es",
"dataset:spanish_large_corpus",
"arxiv:2004.05150",
"transformers",
"Long documents",
"longformer",
"bertin",
"spanish",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | mrm8488 | null | mrm8488/longformer-base-4096-spanish | 17 | 7 | transformers | 9,018 | ---
language:
- es
license: mit
widget:
- text: "Manuel Romero ha creado con el equipo de BERTIN un modelo que procesa documentos <mask> largos."
tags:
- Long documents
- longformer
- bertin
- spanish
datasets:
- spanish_large_corpus
---
# longformer-base-4096-spanish
## [Longformer](https://arxiv.org/abs/2004.05150) is a Transformer model for long documents.
`longformer-base-4096` is a BERT-like model started from the RoBERTa checkpoint (**BERTIN** in this case) and pre-trained for *MLM* on long documents (from BETO's `all_wikis`). It supports sequences of length up to 4,096!
**Longformer** uses a combination of a sliding window (*local*) attention and *global* attention. Global attention is user-configured based on the task to allow the model to learn task-specific representations.
This model was made following the research done by [Iz Beltagy and Matthew E. Peters and Arman Cohan](https://arxiv.org/abs/2004.05150).
## Citation
If you want to cite this model you can use this:
```bibtex
@misc{mromero2022longformer-base-4096-spanish,
title={Spanish LongFormer by Manuel Romero},
author={Romero, Manuel},
publisher={Hugging Face},
journal={Hugging Face Hub},
howpublished={\url{https://huggingface.co/mrm8488/longformer-base-4096-spanish}},
year={2022}
}
``` |
munggok/mt5-large-id-qgen-qa | b2cc736d866cfb585b1096140080532b3ce3cc66 | 2021-01-27T12:55:12.000Z | [
"pytorch",
"t5",
"text2text-generation",
"id",
"dataset:Squad",
"dataset:XQuad",
"dataset:Tydiqa",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | munggok | null | munggok/mt5-large-id-qgen-qa | 17 | null | transformers | 9,019 | ---
language: "id"
license: "mit"
datasets:
- Squad
- XQuad
- Tydiqa
widget:
- text: "I love you"
---
## Prefix use
Use prefix "question: {question} context: {context}" before input to generate the question answering
e.g
"question: siapa nama saya ? context: nama saya andi. saya tinggal di jakarta. istri saya bernama raisa"
for generate question prefix
generate questions: nama saya andi. saya tinggal di jakarta. istri saya bernama raisa
## Training data
Squad
XQuad
Tydiqa |
nlpconnect/dpr-nq-reader-roberta-base-v2 | 6e4e658ff4feec24464ee048f89b26b3b8ff4d05 | 2022-01-03T04:35:47.000Z | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | nlpconnect | null | nlpconnect/dpr-nq-reader-roberta-base-v2 | 17 | null | transformers | 9,020 | Entry not found |
patrickvonplaten/sew-d-mid-400k-librispeech-clean-100h-ft | 4ae68de4dad4afdcf26b02ea022e528ef7ab4278 | 2021-10-27T23:44:33.000Z | [
"pytorch",
"tensorboard",
"sew-d",
"automatic-speech-recognition",
"transformers",
"librispeech_asr",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | patrickvonplaten | null | patrickvonplaten/sew-d-mid-400k-librispeech-clean-100h-ft | 17 | 1 | transformers | 9,021 | ---
license: apache-2.0
tags:
- automatic-speech-recognition
- librispeech_asr
- generated_from_trainer
model-index:
- name: sew-d-mid-400k-librispeech-clean-100h-ft
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. -->
# sew-d-mid-400k-librispeech-clean-100h-ft
This model is a fine-tuned version of [asapp/sew-d-mid-400k](https://huggingface.co/asapp/sew-d-mid-400k) on the LIBRISPEECH_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3540
- Wer: 1.0536
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 7.319 | 0.11 | 100 | 11.0572 | 1.0 |
| 3.6726 | 0.22 | 200 | 4.2003 | 1.0 |
| 2.981 | 0.34 | 300 | 3.5742 | 0.9919 |
| 2.9411 | 0.45 | 400 | 3.2599 | 1.0 |
| 2.903 | 0.56 | 500 | 2.9350 | 1.0 |
| 2.8597 | 0.67 | 600 | 2.9514 | 1.0 |
| 2.7771 | 0.78 | 700 | 2.8521 | 1.0 |
| 2.7926 | 0.9 | 800 | 2.7821 | 1.0120 |
| 2.6623 | 1.01 | 900 | 2.7027 | 0.9924 |
| 2.5893 | 1.12 | 1000 | 2.6667 | 1.0240 |
| 2.5733 | 1.23 | 1100 | 2.6341 | 1.0368 |
| 2.5455 | 1.35 | 1200 | 2.5928 | 1.0411 |
| 2.4919 | 1.46 | 1300 | 2.5695 | 1.0817 |
| 2.5182 | 1.57 | 1400 | 2.5559 | 1.1072 |
| 2.4766 | 1.68 | 1500 | 2.5229 | 1.1257 |
| 2.4267 | 1.79 | 1600 | 2.4991 | 1.1151 |
| 2.3919 | 1.91 | 1700 | 2.4768 | 1.1139 |
| 2.3883 | 2.02 | 1800 | 2.4452 | 1.0636 |
| 2.3737 | 2.13 | 1900 | 2.4304 | 1.0594 |
| 2.3569 | 2.24 | 2000 | 2.4095 | 1.0539 |
| 2.3641 | 2.35 | 2100 | 2.3997 | 1.0511 |
| 2.3281 | 2.47 | 2200 | 2.3856 | 1.0414 |
| 2.2912 | 2.58 | 2300 | 2.3750 | 1.0696 |
| 2.3028 | 2.69 | 2400 | 2.3684 | 1.0436 |
| 2.2906 | 2.8 | 2500 | 2.3613 | 1.0538 |
| 2.2822 | 2.91 | 2600 | 2.3558 | 1.0506 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.13.4.dev0
- Tokenizers 0.10.3
|
patrickvonplaten/wav2vec2-base-timit-fine-tuned | fbe294145f692fa52eccc285e5927b9c7927f8f6 | 2021-10-27T10:49:08.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:timit_asr",
"transformers",
"timit_asr",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | patrickvonplaten | null | patrickvonplaten/wav2vec2-base-timit-fine-tuned | 17 | null | transformers | 9,022 | ---
license: apache-2.0
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: wav2vec2-base-timit-fine-tuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-fine-tuned
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3457
- Wer: 0.2151
## 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: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.1621 | 0.69 | 100 | 3.1102 | 1.0 |
| 2.9592 | 1.38 | 200 | 2.9603 | 1.0 |
| 2.9116 | 2.07 | 300 | 2.8921 | 1.0 |
| 2.1332 | 2.76 | 400 | 1.9718 | 0.9958 |
| 0.8477 | 3.45 | 500 | 0.7813 | 0.5237 |
| 0.4251 | 4.14 | 600 | 0.5166 | 0.3982 |
| 0.3743 | 4.83 | 700 | 0.4400 | 0.3578 |
| 0.4194 | 5.52 | 800 | 0.4077 | 0.3370 |
| 0.23 | 6.21 | 900 | 0.4018 | 0.3142 |
| 0.1554 | 6.9 | 1000 | 0.3623 | 0.2995 |
| 0.1511 | 7.59 | 1100 | 0.3433 | 0.2697 |
| 0.1983 | 8.28 | 1200 | 0.3539 | 0.2715 |
| 0.1443 | 8.97 | 1300 | 0.3622 | 0.2551 |
| 0.0971 | 9.66 | 1400 | 0.3580 | 0.2519 |
| 0.0764 | 10.34 | 1500 | 0.3529 | 0.2437 |
| 0.1203 | 11.03 | 1600 | 0.3455 | 0.2431 |
| 0.0881 | 11.72 | 1700 | 0.3648 | 0.2415 |
| 0.0521 | 12.41 | 1800 | 0.3564 | 0.2320 |
| 0.0434 | 13.1 | 1900 | 0.3485 | 0.2270 |
| 0.0864 | 13.79 | 2000 | 0.3517 | 0.2228 |
| 0.0651 | 14.48 | 2100 | 0.3506 | 0.2285 |
| 0.0423 | 15.17 | 2200 | 0.3428 | 0.2247 |
| 0.0302 | 15.86 | 2300 | 0.3372 | 0.2198 |
| 0.0548 | 16.55 | 2400 | 0.3496 | 0.2196 |
| 0.0674 | 17.24 | 2500 | 0.3407 | 0.2166 |
| 0.0291 | 17.93 | 2600 | 0.3512 | 0.2171 |
| 0.0298 | 18.62 | 2700 | 0.3363 | 0.2158 |
| 0.0419 | 19.31 | 2800 | 0.3493 | 0.2145 |
| 0.046 | 20.0 | 2900 | 0.3457 | 0.2151 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
|
peterhsu/marian-finetuned-kde4-en-to-zh_TW | 1bb82729445285143405f711752f692a65448848 | 2022-02-28T11:26:43.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:kde4",
"transformers",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | translation | false | peterhsu | null | peterhsu/marian-finetuned-kde4-en-to-zh_TW | 17 | null | transformers | 9,023 | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-zh_TW
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
args: en-zh_TW
metrics:
- name: Bleu
type: bleu
value: 39.086345838465
---
<!-- 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. -->
# marian-finetuned-kde4-en-to-zh_TW
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0047
- Bleu: 39.0863
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
philschmid/distilroberta-base-ner-wikiann | 595c043f2d236eda3c67a5fc6ed52f79b3958cf7 | 2022-06-24T11:21:38.000Z | [
"pytorch",
"roberta",
"token-classification",
"dataset:wikiann",
"transformers",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | philschmid | null | philschmid/distilroberta-base-ner-wikiann | 17 | null | transformers | 9,024 | ---
license: apache-2.0
tags:
- token-classification
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilroberta-base-ner-wikiann
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
metrics:
- name: Precision
type: precision
value: 0.8331921416757433
- name: Recall
type: recall
value: 0.84243586083126
- name: F1
type: f1
value: 0.8377885044416501
- name: Accuracy
type: accuracy
value: 0.91930707459758
- task:
type: token-classification
name: Token Classification
dataset:
name: wikiann
type: wikiann
config: en
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.9200373733433721
verified: true
- name: Precision
type: precision
value: 0.9258482820953792
verified: true
- name: Recall
type: recall
value: 0.9347545055892119
verified: true
- name: F1
type: f1
value: 0.9302800779500893
verified: true
- name: loss
type: loss
value: 0.3007512390613556
verified: true
---
<!-- 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-ner-wikiann
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the wikiann dataset.
eval F1-Score: **83,78**
test F1-Score: **83,76**
## Model Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("philschmid/distilroberta-base-ner-wikiann")
model = AutoModelForTokenClassification.from_pretrained("philschmid/distilroberta-base-ner-wikiann")
nlp = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
example = "My name is Philipp and live in Germany"
nlp(example)
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.9086903597787154e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
It achieves the following results on the evaluation set:
- Loss: 0.3156
- Precision: 0.8332
- Recall: 0.8424
- F1: 0.8378
- Accuracy: 0.9193
It achieves the following results on the test set:
- Loss: 0.3023
- Precision: 0.8301
- Recall: 0.8452
- F1: 0.8376
- Accuracy: 0.92
### Framework versions
- Transformers 4.6.1
- Pytorch 1.8.1+cu101
- Datasets 1.6.2
- Tokenizers 0.10.2
|
ricardo-filho/bert-base-portuguese-cased-finetuned-ner | f79cdaa48bdfd404f576c8f2f1a27ec0e5d99da4 | 2021-11-23T13:48:05.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:harem",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | ricardo-filho | null | ricardo-filho/bert-base-portuguese-cased-finetuned-ner | 17 | null | transformers | 9,025 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- harem
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-portuguese-cased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: harem
type: harem
args: default
metrics:
- name: Precision
type: precision
value: 0.0
- name: Recall
type: recall
value: 0.0
- name: F1
type: f1
value: 0.0
- name: Accuracy
type: accuracy
value: 0.7333736396614269
---
<!-- 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-portuguese-cased-finetuned-ner
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the harem dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2948
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.7334
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log | 1.0 | 8 | 1.7381 | 0.0 | 0.0 | 0.0 | 0.7334 |
| No log | 2.0 | 16 | 1.3301 | 0.0 | 0.0 | 0.0 | 0.7334 |
| No log | 3.0 | 24 | 1.2948 | 0.0 | 0.0 | 0.0 | 0.7334 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
sammy786/wav2vec2-xlsr-tatar | c6b788c09ae0d195e8ee66bf2ae119f80470bc71 | 2022-03-23T18:32:40.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"tt",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | sammy786 | null | sammy786/wav2vec2-xlsr-tatar | 17 | null | transformers | 9,026 | ---
language:
- tt
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
- tt
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: sammy786/wav2vec2-xlsr-tatar
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: tt
metrics:
- name: Test WER
type: wer
value: 16.87
- name: Test CER
type: cer
value: 3.64
---
# sammy786/wav2vec2-xlsr-tatar
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - tt dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
- Loss: 7.66
- Wer: 7.08
## Model description
"facebook/wav2vec2-xls-r-1b" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Finnish train.tsv, dev.tsv and other.tsv
## Training procedure
For creating the train dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000045637994662983496
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|-------|---------------|-----------------|----------|
| 200 | 4.849400 | 1.874908 | 0.995232 |
| 400 | 1.105700 | 0.257292 | 0.367658 |
| 600 | 0.723000 | 0.181150 | 0.250513 |
| 800 | 0.660600 | 0.167009 | 0.226078 |
| 1000 | 0.568000 | 0.135090 | 0.177339 |
| 1200 | 0.721200 | 0.117469 | 0.166413 |
| 1400 | 0.416300 | 0.115142 | 0.153765 |
| 1600 | 0.346000 | 0.105782 | 0.153963 |
| 1800 | 0.279700 | 0.102452 | 0.146149 |
| 2000 | 0.273800 | 0.095818 | 0.128468 |
| 2200 | 0.252900 | 0.102302 | 0.133766 |
| 2400 | 0.255100 | 0.096592 | 0.121316 |
| 2600 | 0.229600 | 0.091263 | 0.124561 |
| 2800 | 0.213900 | 0.097748 | 0.125687 |
| 3000 | 0.210700 | 0.091244 | 0.125422 |
| 3200 | 0.202600 | 0.084076 | 0.106284 |
| 3400 | 0.200900 | 0.093809 | 0.113238 |
| 3600 | 0.192700 | 0.082918 | 0.108139 |
| 3800 | 0.182000 | 0.084487 | 0.103371 |
| 4000 | 0.167700 | 0.091847 | 0.104960 |
| 4200 | 0.183700 | 0.085223 | 0.103040 |
| 4400 | 0.174400 | 0.083862 | 0.100589 |
| 4600 | 0.163100 | 0.086493 | 0.099728 |
| 4800 | 0.162000 | 0.081734 | 0.097543 |
| 5000 | 0.153600 | 0.077223 | 0.092974 |
| 5200 | 0.153700 | 0.086217 | 0.090789 |
| 5400 | 0.140200 | 0.093256 | 0.100457 |
| 5600 | 0.142900 | 0.086903 | 0.097742 |
| 5800 | 0.131400 | 0.083068 | 0.095225 |
| 6000 | 0.126000 | 0.086642 | 0.091252 |
| 6200 | 0.135300 | 0.083387 | 0.091186 |
| 6400 | 0.126100 | 0.076479 | 0.086352 |
| 6600 | 0.127100 | 0.077868 | 0.086153 |
| 6800 | 0.118000 | 0.083878 | 0.087676 |
| 7000 | 0.117600 | 0.085779 | 0.091054 |
| 7200 | 0.113600 | 0.084197 | 0.084233 |
| 7400 | 0.112000 | 0.078688 | 0.081319 |
| 7600 | 0.110200 | 0.082534 | 0.086087 |
| 7800 | 0.106400 | 0.077245 | 0.080988 |
| 8000 | 0.102300 | 0.077497 | 0.079332 |
| 8200 | 0.109500 | 0.079083 | 0.088339 |
| 8400 | 0.095900 | 0.079721 | 0.077809 |
| 8600 | 0.094700 | 0.079078 | 0.079730 |
| 8800 | 0.097400 | 0.078785 | 0.079200 |
| 9000 | 0.093200 | 0.077445 | 0.077015 |
| 9200 | 0.088700 | 0.078207 | 0.076617 |
| 9400 | 0.087200 | 0.078982 | 0.076485 |
| 9600 | 0.089900 | 0.081209 | 0.076021 |
| 9800 | 0.081900 | 0.078158 | 0.075757 |
| 10000 | 0.080200 | 0.078074 | 0.074498 |
| 10200 | 0.085000 | 0.078830 | 0.073373 |
| 10400 | 0.080400 | 0.078144 | 0.073373 |
| 10600 | 0.078200 | 0.077163 | 0.073902 |
| 10800 | 0.080900 | 0.076394 | 0.072446 |
| 11000 | 0.080700 | 0.075955 | 0.071585 |
| 11200 | 0.076800 | 0.077031 | 0.072313 |
| 11400 | 0.076300 | 0.077401 | 0.072777 |
| 11600 | 0.076700 | 0.076613 | 0.071916 |
| 11800 | 0.076000 | 0.076672 | 0.071916 |
| 12000 | 0.077200 | 0.076490 | 0.070989 |
| 12200 | 0.076200 | 0.076688 | 0.070856 |
| 12400 | 0.074400 | 0.076780 | 0.071055 |
| 12600 | 0.076300 | 0.076768 | 0.071320 |
| 12800 | 0.077600 | 0.076727 | 0.071055 |
| 13000 | 0.077700 | 0.076714 | 0.071254 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id sammy786/wav2vec2-xlsr-tatar --dataset mozilla-foundation/common_voice_8_0 --config tt --split test
``` |
sebastian-hofstaetter/distilbert-dot-margin_mse-T2-msmarco | f094fd09201e305431b52570d2a9727edf64b394 | 2021-03-16T17:03:58.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"en",
"dataset:ms_marco",
"arxiv:2010.02666",
"transformers",
"dpr",
"dense-passage-retrieval",
"knowledge-distillation"
] | feature-extraction | false | sebastian-hofstaetter | null | sebastian-hofstaetter/distilbert-dot-margin_mse-T2-msmarco | 17 | 1 | transformers | 9,027 | ---
language: "en"
tags:
- dpr
- dense-passage-retrieval
- knowledge-distillation
datasets:
- ms_marco
---
# Margin-MSE Trained DistilBert for Dense Passage Retrieval
We provide a retrieval trained DistilBert-based model (we call the architecture BERT_Dot). Our model is trained with Margin-MSE using a 3 teacher BERT_Cat (concatenated BERT scoring) ensemble on MSMARCO-Passage.
This instance can be used to **re-rank a candidate set** or **directly for a vector index based dense retrieval**. The architecture is a 6-layer DistilBERT, without architecture additions or modifications (we only change the weights during training) - to receive a query/passage representation we pool the CLS vector. We use the same BERT layers for both query and passage encoding (yields better results, and lowers memory requirements).
If you want to know more about our simple, yet effective knowledge distillation method for efficient information retrieval models for a variety of student architectures that is used for this model instance check out our paper: https://arxiv.org/abs/2010.02666 🎉
For more information, training data, source code, and a minimal usage example please visit: https://github.com/sebastian-hofstaetter/neural-ranking-kd
## Effectiveness on MSMARCO Passage & TREC-DL'19
We trained our model on the MSMARCO standard ("small"-400K query) training triples with knowledge distillation with a batch size of 32 on a single consumer-grade GPU (11GB memory).
For re-ranking we used the top-1000 BM25 results.
### MSMARCO-DEV
| | MRR@10 | NDCG@10 | Recall@1K |
|----------------------------------|--------|---------|-----------------------------|
| BM25 | .194 | .241 | .868 |
| **Margin-MSE BERT_Dot** (Re-ranking) | .332 | .391 | .868 (from BM25 candidates) |
| **Margin-MSE BERT_Dot** (Retrieval) | .323 | .381 | .957 |
### TREC-DL'19
For MRR and Recall we use the recommended binarization point of the graded relevance of 2. This might skew the results when compared to other binarization point numbers.
| | MRR@10 | NDCG@10 | Recall@1K |
|----------------------------------|--------|---------|-----------------------------|
| BM25 | .689 | .501 | .739 |
| **Margin-MSE BERT_Dot** (Re-ranking) | .862 | .712 | .739 (from BM25 candidates) |
| **Margin-MSE BERT_Dot** (Retrieval) | .868 | .697 | .769 |
For more baselines, info and analysis, please see the paper: https://arxiv.org/abs/2010.02666
## Limitations & Bias
- The model inherits social biases from both DistilBERT and MSMARCO.
- The model is only trained on relatively short passages of MSMARCO (avg. 60 words length), so it might struggle with longer text.
## Citation
If you use our model checkpoint please cite our work as:
```
@misc{hofstaetter2020_crossarchitecture_kd,
title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation},
author={Sebastian Hofst{\"a}tter and Sophia Althammer and Michael Schr{\"o}der and Mete Sertkan and Allan Hanbury},
year={2020},
eprint={2010.02666},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
``` |
seongju/kor-3i4k-bert-base-cased | 12c1152e20a9d293985fa077c90a723bd3257ff4 | 2021-07-20T07:58:11.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | seongju | null | seongju/kor-3i4k-bert-base-cased | 17 | null | transformers | 9,028 | ### Model information
* language : Korean
* fine tuning data : [kor_3i4k](https://huggingface.co/datasets/kor_3i4k)
* License : CC-BY-SA 4.0
* Base model : [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
* input : sentence
* output : intent
----
### Train information
* train_runtime: 2376.638
* train_steps_per_second: 2.175
* train_loss: 0.356829648599977
* epoch: 3.0
----
### How to use
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained (
"seongju/kor-3i4k-bert-base-cased"
)
model = AutoModelForSequenceClassification.from_pretrained (
"seongju/kor-3i4k-bert-base-cased"
)
inputs = tokenizer(
"너는 지금 무엇을 하고 있니?",
padding=True, truncation=True, max_length=128, return_tensors="pt"
)
outputs = model(**inputs)
probs = outputs[0].softmax(1)
output = probs.argmax().item()
``` |
shamikbose89/mt5-small-finetuned-arxiv-cs-finetuned-arxiv-cs-full | 7a8420078c15eb05d48aa4a5cbb095c09a11779a | 2021-11-19T17:54:25.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"summarization",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | shamikbose89 | null | shamikbose89/mt5-small-finetuned-arxiv-cs-finetuned-arxiv-cs-full | 17 | 5 | transformers | 9,029 | ---
license: apache-2.0
tags:
- generated_from_trainer
- summarization
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-arxiv-cs-finetuned-arxiv-cs-full
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-arxiv-cs-finetuned-arxiv-cs-full
This model is a fine-tuned version of [shamikbose89/mt5-small-finetuned-arxiv-cs](https://huggingface.co/shamikbose89/mt5-small-finetuned-arxiv-cs) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4037
- Rouge1: 39.8923
- Rouge2: 20.9831
- Rougel: 35.8642
- Rougelsum: 35.8511
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 1.9675 | 1.0 | 500 | 1.5573 | 36.4989 | 18.4839 | 33.2984 | 33.2917 |
| 1.7523 | 2.0 | 1000 | 1.4972 | 37.7911 | 19.0357 | 33.5725 | 33.6058 |
| 1.6611 | 3.0 | 1500 | 1.4593 | 38.5822 | 19.4928 | 34.215 | 34.2531 |
| 1.6187 | 4.0 | 2000 | 1.4492 | 39.1219 | 20.8705 | 35.1969 | 35.2255 |
| 1.5864 | 5.0 | 2500 | 1.4289 | 39.7304 | 21.0654 | 35.6602 | 35.6667 |
| 1.5553 | 6.0 | 3000 | 1.4184 | 40.0696 | 21.0883 | 35.9536 | 35.9132 |
| 1.5215 | 7.0 | 3500 | 1.4163 | 39.1956 | 20.6757 | 35.5016 | 35.5196 |
| 1.5038 | 8.0 | 4000 | 1.4148 | 39.2373 | 20.3114 | 35.1676 | 35.1532 |
| 1.4929 | 9.0 | 4500 | 1.4064 | 39.9249 | 21.0155 | 35.8247 | 35.7937 |
| 1.4791 | 10.0 | 5000 | 1.4037 | 39.8923 | 20.9831 | 35.8642 | 35.8511 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
shuqi/seed-encoder | f3ca2a12f7ac5921d0d9793ec9e9fb03e6f19aba | 2021-09-18T11:24:50.000Z | [
"pytorch",
"seed_encoder",
"transformers"
] | null | false | shuqi | null | shuqi/seed-encoder | 17 | null | transformers | 9,030 | # Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using a Weak Decoder
Please check the [official repository](https://github.com/microsoft/SEED-Encoder) for more details and updates.
# Fine-tuning on Marco passage/doc ranking tasks and NQ tasks
| MSMARCO Dev Passage Retrieval | MRR@10 | Recall@1k |
|------------------------------|---------------|--------------------- |
| BM25 warmup checkpoint | 0.329 | 0.953 |
| ANCE Passage checkpoint | 0.334 | 0.961 |
| MSMARCO Document Retrieval | MRR@10 (Dev) | MRR@10 (Eval) |
|---------------- | -------------- | -------------- |
| ANCE Document (FirstP) checkpoint | 0.394 | 0.362 |
| NQ Task | Top-1 | Top-5 | Top-20 | Top-100 | MRR@20 | P@20 |
|---------------- | -------------- | -------------- |-------------- | -------------- | -------------- |-------------- |
| DPR checkpoint | 46.1 | 68.8 | 80.4 | 87.1 | 56.2 | 20.1 |
| ANCE NQ checkpoint | 52.5 | 73.1 | 83.1 | 88.7 | 61.5 | 22.5
# Citation
If you find SEED-Encoder useful for your work, please cite the following paper:
```
@article{lu2021less,
title={Less is More: Pre-training a Strong Siamese Encoder Using a Weak Decoder},
author={Lu, Shuqi and Xiong, Chenyan and He, Di and Ke, Guolin and Malik, Waleed and Dou, Zhicheng and Bennett, Paul and Liu, Tieyan and Overwijk, Arnold},
journal={arXiv preprint arXiv:2102.09206},
year={2021}
}
```
|
sismetanin/mbart_ru_sum_gazeta-ru-sentiment-rureviews | 4fedb6d31035d2017f4cb8e2758032035e93ffc1 | 2021-02-25T23:49:57.000Z | [
"pytorch",
"mbart",
"text-classification",
"ru",
"transformers",
"sentiment analysis",
"Russian"
] | text-classification | false | sismetanin | null | sismetanin/mbart_ru_sum_gazeta-ru-sentiment-rureviews | 17 | null | transformers | 9,031 | ---
language:
- ru
tags:
- sentiment analysis
- Russian
---
## MBARTRuSumGazeta-ru-sentiment-RuReviews
MBARTRuSumGazeta-ru-sentiment-RuReviews is a [MBARTRuSumGazeta](https://huggingface.co/IlyaGusev/mbart_ru_sum_gazeta) model fine-tuned on [RuReviews dataset](https://github.com/sismetanin/rureviews) of Russian-language reviews from the ”Women’s Clothes and Accessories” product category on the primary e-commerce site in Russia.
<table>
<thead>
<tr>
<th rowspan="4">Model</th>
<th rowspan="4">Score<br></th>
<th rowspan="4">Rank</th>
<th colspan="12">Dataset</th>
</tr>
<tr>
<td colspan="6">SentiRuEval-2016<br></td>
<td colspan="2" rowspan="2">RuSentiment</td>
<td rowspan="2">KRND</td>
<td rowspan="2">LINIS Crowd</td>
<td rowspan="2">RuTweetCorp</td>
<td rowspan="2">RuReviews</td>
</tr>
<tr>
<td colspan="3">TC</td>
<td colspan="3">Banks</td>
</tr>
<tr>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>wighted</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
</tr>
</thead>
<tbody>
<tr>
<td>SOTA</td>
<td>n/s</td>
<td></td>
<td>76.71</td>
<td>66.40</td>
<td>70.68</td>
<td>67.51</td>
<td>69.53</td>
<td>74.06</td>
<td>78.50</td>
<td>n/s</td>
<td>73.63</td>
<td>60.51</td>
<td>83.68</td>
<td>77.44</td>
</tr>
<tr>
<td>XLM-RoBERTa-Large</td>
<td>76.37</td>
<td>1</td>
<td>82.26</td>
<td>76.36</td>
<td>79.42</td>
<td>76.35</td>
<td>76.08</td>
<td>80.89</td>
<td>78.31</td>
<td>75.27</td>
<td>75.17</td>
<td>60.03</td>
<td>88.91</td>
<td>78.81</td>
</tr>
<tr>
<td>SBERT-Large</td>
<td>75.43</td>
<td>2</td>
<td>78.40</td>
<td>71.36</td>
<td>75.14</td>
<td>72.39</td>
<td>71.87</td>
<td>77.72</td>
<td>78.58</td>
<td>75.85</td>
<td>74.20</td>
<td>60.64</td>
<td>88.66</td>
<td>77.41</td>
</tr>
<tr>
<td>MBARTRuSumGazeta</td>
<td>74.70</td>
<td>3</td>
<td>76.06</td>
<td>68.95</td>
<td>73.04</td>
<td>72.34</td>
<td>71.93</td>
<td>77.83</td>
<td>76.71</td>
<td>73.56</td>
<td>74.18</td>
<td>60.54</td>
<td>87.22</td>
<td>77.51</td>
</tr>
<tr>
<td>Conversational RuBERT</td>
<td>74.44</td>
<td>4</td>
<td>76.69</td>
<td>69.09</td>
<td>73.11</td>
<td>69.44</td>
<td>68.68</td>
<td>75.56</td>
<td>77.31</td>
<td>74.40</td>
<td>73.10</td>
<td>59.95</td>
<td>87.86</td>
<td>77.78</td>
</tr>
<tr>
<td>LaBSE</td>
<td>74.11</td>
<td>5</td>
<td>77.00</td>
<td>69.19</td>
<td>73.55</td>
<td>70.34</td>
<td>69.83</td>
<td>76.38</td>
<td>74.94</td>
<td>70.84</td>
<td>73.20</td>
<td>59.52</td>
<td>87.89</td>
<td>78.47</td>
</tr>
<tr>
<td>XLM-RoBERTa-Base</td>
<td>73.60</td>
<td>6</td>
<td>76.35</td>
<td>69.37</td>
<td>73.42</td>
<td>68.45</td>
<td>67.45</td>
<td>74.05</td>
<td>74.26</td>
<td>70.44</td>
<td>71.40</td>
<td>60.19</td>
<td>87.90</td>
<td>78.28</td>
</tr>
<tr>
<td>RuBERT</td>
<td>73.45</td>
<td>7</td>
<td>74.03</td>
<td>66.14</td>
<td>70.75</td>
<td>66.46</td>
<td>66.40</td>
<td>73.37</td>
<td>75.49</td>
<td>71.86</td>
<td>72.15</td>
<td>60.55</td>
<td>86.99</td>
<td>77.41</td>
</tr>
<tr>
<td>MBART-50-Large-Many-to-Many</td>
<td>73.15</td>
<td>8</td>
<td>75.38</td>
<td>67.81</td>
<td>72.26</td>
<td>67.13</td>
<td>66.97</td>
<td>73.85</td>
<td>74.78</td>
<td>70.98</td>
<td>71.98</td>
<td>59.20</td>
<td>87.05</td>
<td>77.24</td>
</tr>
<tr>
<td>SlavicBERT</td>
<td>71.96</td>
<td>9</td>
<td>71.45</td>
<td>63.03</td>
<td>68.44</td>
<td>64.32</td>
<td>63.99</td>
<td>71.31</td>
<td>72.13</td>
<td>67.57</td>
<td>72.54</td>
<td>58.70</td>
<td>86.43</td>
<td>77.16</td>
</tr>
<tr>
<td>EnRuDR-BERT</td>
<td>71.51</td>
<td>10</td>
<td>72.56</td>
<td>64.74</td>
<td>69.07</td>
<td>61.44</td>
<td>60.21</td>
<td>68.34</td>
<td>74.19</td>
<td>69.94</td>
<td>69.33</td>
<td>56.55</td>
<td>87.12</td>
<td>77.95</td>
</tr>
<tr>
<td>RuDR-BERT</td>
<td>71.14</td>
<td>11</td>
<td>72.79</td>
<td>64.23</td>
<td>68.36</td>
<td>61.86</td>
<td>60.92</td>
<td>68.48</td>
<td>74.65</td>
<td>70.63</td>
<td>68.74</td>
<td>54.45</td>
<td>87.04</td>
<td>77.91</td>
</tr>
<tr>
<td>MBART-50-Large</td>
<td>69.46</td>
<td>12</td>
<td>70.91</td>
<td>62.67</td>
<td>67.24</td>
<td>61.12</td>
<td>60.25</td>
<td>68.41</td>
<td>72.88</td>
<td>68.63</td>
<td>70.52</td>
<td>46.39</td>
<td>86.48</td>
<td>77.52</td>
</tr>
</tbody>
</table>
The table shows per-task scores and a macro-average of those scores to determine a models’s position on the leaderboard. For datasets with multiple evaluation metrics (e.g., macro F1 and weighted F1 for RuSentiment), we use an unweighted average of the metrics as the score for the task when computing the overall macro-average. The same strategy for comparing models’ results was applied in the GLUE benchmark.
## Citation
If you find this repository helpful, feel free to cite our publication:
```
@article{Smetanin2021Deep,
author = {Sergey Smetanin and Mikhail Komarov},
title = {Deep transfer learning baselines for sentiment analysis in Russian},
journal = {Information Processing & Management},
volume = {58},
number = {3},
pages = {102484},
year = {2021},
issn = {0306-4573},
doi = {0.1016/j.ipm.2020.102484}
}
```
Dataset:
```
@INPROCEEDINGS{Smetanin2019Sentiment,
author={Sergey Smetanin and Michail Komarov},
booktitle={2019 IEEE 21st Conference on Business Informatics (CBI)},
title={Sentiment Analysis of Product Reviews in Russian using Convolutional Neural Networks},
year={2019},
volume={01},
pages={482-486},
doi={10.1109/CBI.2019.00062},
ISSN={2378-1963},
month={July}
}
``` |
stanford-crfm/battlestar-gpt2-small-x49 | 2f4e2079c9ac92c2b5c6fecc19fae645bcef56fa | 2022-06-20T09:04:32.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | stanford-crfm | null | stanford-crfm/battlestar-gpt2-small-x49 | 17 | null | transformers | 9,032 | Entry not found |
subbareddyiiit/TeElectra | 5ec4c5d8a5fa681713005efc391e26e05726f0e6 | 2020-06-21T06:59:39.000Z | [
"pytorch",
"electra",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | subbareddyiiit | null | subbareddyiiit/TeElectra | 17 | null | transformers | 9,033 | Entry not found |
tals/albert-base-vitaminc_flagging | 1e5f38d76c4d9402bf0c7d73e1aab6eaafca0ea8 | 2022-06-22T23:56:43.000Z | [
"pytorch",
"albert",
"text-classification",
"python",
"dataset:fever",
"dataset:glue",
"dataset:tals/vitaminc",
"transformers"
] | text-classification | false | tals | null | tals/albert-base-vitaminc_flagging | 17 | null | transformers | 9,034 | ---
language: python
datasets:
- fever
- glue
- tals/vitaminc
---
# Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this model, please cite the paper.
# BibTeX entry and citation info
```bibtex
@inproceedings{schuster-etal-2021-get,
title = "Get Your Vitamin {C}! Robust Fact Verification with Contrastive Evidence",
author = "Schuster, Tal and
Fisch, Adam and
Barzilay, Regina",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.52",
doi = "10.18653/v1/2021.naacl-main.52",
pages = "624--643",
abstract = "Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as more information is gathered and revised. In order to adapt, models must be sensitive to subtle differences in supporting evidence. We present VitaminC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes. We collect over 100,000 Wikipedia revisions that modify an underlying fact, and leverage these revisions, together with additional synthetically constructed ones, to create a total of over 400,000 claim-evidence pairs. Unlike previous resources, the examples in VitaminC are contrastive, i.e., they contain evidence pairs that are nearly identical in language and content, with the exception that one supports a given claim while the other does not. We show that training using this design increases robustness{---}improving accuracy by 10{\%} on adversarial fact verification and 6{\%} on adversarial natural language inference (NLI). Moreover, the structure of VitaminC leads us to define additional tasks for fact-checking resources: tagging relevant words in the evidence for verifying the claim, identifying factual revisions, and providing automatic edits via factually consistent text generation.",
}
```
|
trnt/twitter_emotions | 0fdc42320272eddfe43aa03670ac20c5028a7e9a | 2021-11-20T04:31:53.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | trnt | null | trnt/twitter_emotions | 17 | 1 | transformers | 9,035 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: twitter_emotions
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9375
---
<!-- 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_emotions
This model is a fine-tuned version of [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1647
- Accuracy: 0.9375
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2486 | 1.0 | 2000 | 0.2115 | 0.931 |
| 0.135 | 2.0 | 4000 | 0.1725 | 0.936 |
| 0.1041 | 3.0 | 6000 | 0.1647 | 0.9375 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
turtlesoupy/inverse-dictionary-model-v1 | 485568f794dce00946739bf86e31841623655087 | 2021-05-23T13:17:21.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | turtlesoupy | null | turtlesoupy/inverse-dictionary-model-v1 | 17 | null | transformers | 9,036 | Entry not found |
yhavinga/gpt-neo-1.3B-dutch | 02db444ac45c0ed6dfebf10010eeab7fb3a1a0ae | 2022-03-20T10:20:34.000Z | [
"pytorch",
"jax",
"tensorboard",
"gpt_neo",
"text-generation",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"transformers",
"gpt-neo-1.3B",
"gpt-neo"
] | text-generation | false | yhavinga | null | yhavinga/gpt-neo-1.3B-dutch | 17 | null | transformers | 9,037 | ---
language: nl
widget:
- text: "In het jaar 2030 zullen we"
- text: "Toen ik gisteren volledig in de ban was van"
- text: "Studenten en leraren van de Bogazici Universiteit in de Turkse stad Istanbul"
- text: "In Israël was een strenge lockdown"
tags:
- gpt-neo-1.3B
- gpt-neo
pipeline_tag: text-generation
datasets:
- yhavinga/mc4_nl_cleaned
---
# GPT Neo 1.3B pre-trained on cleaned Dutch mC4 🇳🇱
A GPT-Neo model trained from scratch on Dutch, with perplexity 16.0 on cleaned Dutch mC4.
## How To Use
You can use this GPT-Neo model directly with a pipeline for text generation.
```python
MODEL_DIR='yhavinga/gpt-neo-1.3B-dutch'
from transformers import pipeline, GPT2Tokenizer, GPTNeoForCausalLM
tokenizer = GPT2Tokenizer.from_pretrained(MODEL_DIR)
model = GPTNeoForCausalLM.from_pretrained(MODEL_DIR)
generator = pipeline('text-generation', model, tokenizer=tokenizer)
generated_text = generator('1 - geel. 2 - groen. 3 -', max_length=60, num_beams=4, no_repeat_ngram_size=3, repetition_penalty=2.0)
```
*"1 - geel. 2 - groen. 3 - rood. 4 - blauw. 5 - bruin. 6 - zwart. 7 - oranje. 8 - roze. 9 - paars. 10 - wit. 11 - grijs. 12 - magenta. 13 - lila. 14 - lichtgroen. 15"*
## Tokenizer
* BPE tokenizer trained from scratch for Dutch on mC4 nl cleaned with scripts from the Huggingface
Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling).
## Dataset
This model was trained on the `full` configuration (33B tokens) of [cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
which is the original mC4, except
* Documents that contained words from a selection of the Dutch and English [List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) are removed
* Sentences with less than 3 words are removed
* Sentences with a word of more than 1000 characters are removed
* Documents with less than 5 sentences are removed
* Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies",
"use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.
## Models
TL;DR: [yhavinga/gpt2-medium-dutch](https://huggingface.co/yhavinga/gpt2-medium-dutch) is the best model.
* The models with `a`/`b` in the step-column have been trained to step `a` of a total of `b` steps.
| | model | params | train seq len | ppl | loss | batch size | epochs | steps | optim | lr | duration | config |
|-----------------------------------------------------------------------------------|---------|--------|---------------|------|------|------------|--------|-----------------|-----------|--------|----------|-----------|
| [yhavinga/gpt-neo-125M-dutch](https://huggingface.co/yhavinga/gpt-neo-125M-dutch) | gpt neo | 125M | 512 | 20.9 | 3.04 | 128 | 1 | 190000/558608 | adam | 2.4e-3 | 1d 12h | full |
| [yhavinga/gpt2-medium-dutch](https://huggingface.co/yhavinga/gpt2-medium-dutch) | gpt2 | 345M | 512 | 15.1 | 2.71 | 128 | 1 | 320000/520502 | adam | 8e-4 | 7d 2h | full |
| [yhavinga/gpt2-large-dutch](https://huggingface.co/yhavinga/gpt2-large-dutch) | gpt2 | 762M | 512 | 15.1 | 2.72 | 32 | 1 | 1100000/2082009 | adafactor | 3.3e-5 | 8d 15h | large |
| [yhavinga/gpt-neo-1.3B-dutch](https://huggingface.co/yhavinga/gpt-neo-1.3B-dutch) | gpt neo | 1.3B | 512 | 16.0 | 2.77 | 16 | 1 | 960000/3049896 | adafactor | 5e-4 | 7d 11h | full |
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem was also
instrumental in most, if not all, parts of the training. The following repositories where helpful in setting up the TPU-VM,
and training the models:
* [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
* [HUggingFace Flax MLM examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
* [gpt2-medium-persian](https://huggingface.co/flax-community/gpt2-medium-persian)
* [gpt2-medium-indonesian](https://huggingface.co/flax-community/gpt2-medium-persian)
Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)
|
bookbot/distil-wav2vec2-adult-child-cls-37m | 80548f793c175d52787f726302db721c6fd25bf8 | 2022-02-26T14:49:52.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"en",
"arxiv:2006.11477",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | audio-classification | false | bookbot | null | bookbot/distil-wav2vec2-adult-child-cls-37m | 17 | null | transformers | 9,038 | ---
language: en
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distil-wav2vec2-adult-child-cls-37m
results: []
---
# DistilWav2Vec2 Adult/Child Speech Classifier 37M
DistilWav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the [wav2vec 2.0](https://arxiv.org/abs/2006.11477) architecture. This model is a distilled version of [wav2vec2-adult-child-cls](https://huggingface.co/bookbot/wav2vec2-adult-child-cls) on a private adult/child speech classification dataset.
This model was trained using HuggingFace's PyTorch framework. All training was done on a Tesla P100, provided by Kaggle. Training metrics were logged via Tensorboard.
## Model
| Model | #params | Arch. | Training/Validation data (text) |
| ------------------------------------- | ------- | ----------- | ----------------------------------------- |
| `distil-wav2vec2-adult-child-cls-37m` | 37M | wav2vec 2.0 | Adult/Child Speech Classification Dataset |
## Evaluation Results
The model achieves the following results on evaluation:
| Dataset | Loss | Accuracy | F1 |
| --------------------------------- | ------ | -------- | ------ |
| Adult/Child Speech Classification | 0.1431 | 95.89% | 0.9624 |
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- `learning_rate`: 3e-05
- `train_batch_size`: 32
- `eval_batch_size`: 32
- `seed`: 42
- `gradient_accumulation_steps`: 4
- `total_train_batch_size`: 128
- `optimizer`: Adam with `betas=(0.9,0.999)` and `epsilon=1e-08`
- `lr_scheduler_type`: linear
- `lr_scheduler_warmup_ratio`: 0.1
- `num_epochs`: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
| :-----------: | :---: | :--: | :-------------: | :------: | :----: |
| 0.2586 | 1.0 | 96 | 0.2257 | 0.9298 | 0.9363 |
| 0.1917 | 2.0 | 192 | 0.1743 | 0.9460 | 0.9500 |
| 0.1568 | 3.0 | 288 | 0.1701 | 0.9511 | 0.9545 |
| 0.0965 | 4.0 | 384 | 0.1501 | 0.9548 | 0.9584 |
| 0.1179 | 5.0 | 480 | 0.1431 | 0.9589 | 0.9624 |
## Disclaimer
Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.
## Authors
DistilWav2Vec2 Adult/Child Speech Classifier was trained and evaluated by [Ananto Joyoadikusumo](https://anantoj.github.io/). All computation and development are done on Kaggle.
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3 |
abdusahmbzuai/arabert-ner | d0903445372670c4402f3441ac3723c9dcfc5bc0 | 2022-03-01T15:53:14.000Z | [
"pytorch",
"bert",
"token-classification",
"ar",
"dataset:wikiann",
"transformers",
"ner",
"classification",
"autotrain_compatible"
] | token-classification | false | abdusahmbzuai | null | abdusahmbzuai/arabert-ner | 17 | 1 | transformers | 9,039 |
---
pipeline_tag: token-classification
language: ar
datasets:
- wikiann
task_ids:
- named-entity-recognition
tags:
- "ner"
- "ar"
- "classification"
widget:
- text: "كريستيانو رونالدو يلعب مع نادي يوفنتوس"
example_title: "Sentence 1"
- text: "تخرج أحمد من الجامعة الأمريكية في الشارقة الشهر الماضي"
example_title: "Sentence 2"
- text: "لا يزال ديبالا يلعب لفريق يوفنتوس"
example_title: "Sentence 3"
---
# Arabic NER |
davanstrien/convnext_flyswot | ba93cdfc85a8cc69f491717f7f184a03cbca78d8 | 2022-03-01T20:47:35.000Z | [
"pytorch",
"convnext",
"image-classification",
"dataset:image_folder",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | davanstrien | null | davanstrien/convnext_flyswot | 17 | null | transformers | 9,040 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- f1
model-index:
- name: convnext_flyswot
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: F1
type: f1
value: 0.959245529738118
---
<!-- 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_flyswot
This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext-base-224-22k) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1441
- F1: 0.9592
## 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: 666
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 52 | 0.6833 | 0.7484 |
| No log | 2.0 | 104 | 0.3666 | 0.8750 |
| No log | 3.0 | 156 | 0.2090 | 0.9321 |
| No log | 4.0 | 208 | 0.1478 | 0.9449 |
| No log | 5.0 | 260 | 0.1002 | 0.9518 |
| No log | 6.0 | 312 | 0.1053 | 0.9506 |
| No log | 7.0 | 364 | 0.1182 | 0.9616 |
| No log | 8.0 | 416 | 0.1102 | 0.9592 |
| No log | 9.0 | 468 | 0.1262 | 0.9616 |
| 0.203 | 10.0 | 520 | 0.1286 | 0.9616 |
| 0.203 | 11.0 | 572 | 0.1355 | 0.9592 |
| 0.203 | 12.0 | 624 | 0.1299 | 0.9592 |
| 0.203 | 13.0 | 676 | 0.1154 | 0.9592 |
| 0.203 | 14.0 | 728 | 0.1385 | 0.9580 |
| 0.203 | 15.0 | 780 | 0.1330 | 0.9592 |
| 0.203 | 16.0 | 832 | 0.1390 | 0.9592 |
| 0.203 | 17.0 | 884 | 0.1386 | 0.9592 |
| 0.203 | 18.0 | 936 | 0.1390 | 0.9592 |
| 0.203 | 19.0 | 988 | 0.1409 | 0.9592 |
| 0.0006 | 20.0 | 1040 | 0.1411 | 0.9592 |
| 0.0006 | 21.0 | 1092 | 0.1413 | 0.9592 |
| 0.0006 | 22.0 | 1144 | 0.1415 | 0.9592 |
| 0.0006 | 23.0 | 1196 | 0.1426 | 0.9592 |
| 0.0006 | 24.0 | 1248 | 0.1435 | 0.9592 |
| 0.0006 | 25.0 | 1300 | 0.1438 | 0.9592 |
| 0.0006 | 26.0 | 1352 | 0.1434 | 0.9592 |
| 0.0006 | 27.0 | 1404 | 0.1437 | 0.9592 |
| 0.0006 | 28.0 | 1456 | 0.1441 | 0.9592 |
| 0.0002 | 29.0 | 1508 | 0.1440 | 0.9592 |
| 0.0002 | 30.0 | 1560 | 0.1441 | 0.9592 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
davanstrien/flyswot_iiif | d8b0a089e42854c5c5f5129ecfc83a8285d45670 | 2022-03-02T07:59:30.000Z | [
"pytorch",
"convnext",
"image-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | davanstrien | null | davanstrien/flyswot_iiif | 17 | null | transformers | 9,041 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: flyswot_iiif
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. -->
# flyswot_iiif
This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext-base-224-22k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.1280
- F1: 0.0034
## 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: 666
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 8.5184 | 0.26 | 500 | 7.9280 | 0.0005 |
| 7.7409 | 0.52 | 1000 | 7.5824 | 0.0007 |
| 7.4649 | 0.78 | 1500 | 7.3841 | 0.0010 |
| 7.3285 | 1.04 | 2000 | 7.2652 | 0.0012 |
| 7.1404 | 1.3 | 2500 | 7.1559 | 0.0014 |
| 7.0322 | 1.56 | 3000 | 7.0551 | 0.0016 |
| 6.9197 | 1.82 | 3500 | 6.9449 | 0.0019 |
| 6.7822 | 2.09 | 4000 | 6.8773 | 0.0018 |
| 6.6506 | 2.35 | 4500 | 6.7980 | 0.0020 |
| 6.5811 | 2.61 | 5000 | 6.7382 | 0.0022 |
| 6.538 | 2.87 | 5500 | 6.6582 | 0.0022 |
| 6.4136 | 3.13 | 6000 | 6.6013 | 0.0024 |
| 6.3325 | 3.39 | 6500 | 6.5369 | 0.0024 |
| 6.2566 | 3.65 | 7000 | 6.4875 | 0.0025 |
| 6.2285 | 3.91 | 7500 | 6.4342 | 0.0027 |
| 6.1281 | 4.17 | 8000 | 6.4066 | 0.0027 |
| 6.0762 | 4.43 | 8500 | 6.3674 | 0.0027 |
| 6.0309 | 4.69 | 9000 | 6.3336 | 0.0027 |
| 6.0123 | 4.95 | 9500 | 6.2932 | 0.0030 |
| 5.9089 | 5.21 | 10000 | 6.2835 | 0.0029 |
| 5.8901 | 5.47 | 10500 | 6.2481 | 0.0030 |
| 5.86 | 5.74 | 11000 | 6.2295 | 0.0030 |
| 5.8586 | 6.0 | 11500 | 6.2068 | 0.0033 |
| 5.7768 | 6.26 | 12000 | 6.1937 | 0.0031 |
| 5.7591 | 6.52 | 12500 | 6.1916 | 0.0032 |
| 5.7443 | 6.78 | 13000 | 6.1579 | 0.0033 |
| 5.7125 | 7.04 | 13500 | 6.1478 | 0.0033 |
| 5.6751 | 7.3 | 14000 | 6.1379 | 0.0035 |
| 5.6648 | 7.56 | 14500 | 6.1304 | 0.0035 |
| 5.6644 | 7.82 | 15000 | 6.1280 | 0.0034 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
Ameer05/bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch-tweak-lr-8-100-1 | c0b4d0d486b0ffe8c8cf79ecf7001bb7a2090794 | 2022-03-08T16:43:01.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"transformers",
"summarization",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | summarization | false | Ameer05 | null | Ameer05/bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch-tweak-lr-8-100-1 | 17 | null | transformers | 9,042 | ---
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch-tweak-lr-8-100-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch-tweak-lr-8-100-1
This model is a fine-tuned version of [Ameer05/model-token-repo](https://huggingface.co/Ameer05/model-token-repo) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6315
- Rouge1: 61.441
- Rouge2: 52.9403
- Rougel: 58.3426
- Rougelsum: 60.8249
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| No log | 0.91 | 5 | 2.0139 | 53.4301 | 46.6698 | 50.644 | 53.3985 |
| No log | 1.91 | 10 | 1.6309 | 61.4629 | 53.8884 | 59.0867 | 60.8823 |
| No log | 2.91 | 15 | 1.5379 | 61.2938 | 53.7208 | 59.0644 | 60.7381 |
| No log | 3.91 | 20 | 1.4470 | 63.2667 | 55.9273 | 60.5112 | 62.7538 |
| 1.5454 | 4.91 | 25 | 1.4353 | 62.7166 | 54.8328 | 60.0101 | 62.1378 |
| 1.5454 | 5.91 | 30 | 1.4411 | 59.7469 | 51.9068 | 57.036 | 58.9474 |
| 1.5454 | 6.91 | 35 | 1.5195 | 64.152 | 57.1447 | 61.362 | 63.5951 |
| 1.5454 | 7.91 | 40 | 1.6174 | 60.1464 | 51.5654 | 57.1676 | 59.4405 |
| 0.5429 | 8.91 | 45 | 1.7451 | 61.9696 | 53.6421 | 58.5884 | 61.3286 |
| 0.5429 | 9.91 | 50 | 1.9081 | 60.3296 | 52.3052 | 57.6518 | 59.7854 |
| 0.5429 | 10.91 | 55 | 1.9721 | 61.5597 | 51.9027 | 57.1184 | 60.6717 |
| 0.5429 | 11.91 | 60 | 2.0471 | 61.2222 | 53.9475 | 58.725 | 60.6668 |
| 0.5429 | 12.91 | 65 | 2.1422 | 60.1915 | 52.0627 | 56.9955 | 59.438 |
| 0.1506 | 13.91 | 70 | 2.1542 | 61.6915 | 53.045 | 58.1727 | 60.8765 |
| 0.1506 | 14.91 | 75 | 2.1885 | 59.8069 | 51.6543 | 56.8112 | 59.2055 |
| 0.1506 | 15.91 | 80 | 2.3146 | 61.695 | 53.2666 | 57.9003 | 61.1108 |
| 0.1506 | 16.91 | 85 | 2.3147 | 60.4482 | 52.1694 | 57.0649 | 59.7882 |
| 0.0452 | 17.91 | 90 | 2.1731 | 60.0259 | 51.5046 | 56.7399 | 59.2955 |
| 0.0452 | 18.91 | 95 | 2.2690 | 60.0534 | 52.4819 | 57.1631 | 59.5056 |
| 0.0452 | 19.91 | 100 | 2.2990 | 58.0737 | 48.8098 | 54.5684 | 57.3187 |
| 0.0452 | 20.91 | 105 | 2.2704 | 61.8982 | 53.9077 | 58.6909 | 61.4252 |
| 0.0267 | 21.91 | 110 | 2.3012 | 62.0174 | 53.5427 | 58.5278 | 61.1921 |
| 0.0267 | 22.91 | 115 | 2.3569 | 61.6327 | 53.7387 | 58.8908 | 61.1623 |
| 0.0267 | 23.91 | 120 | 2.3579 | 60.228 | 52.3747 | 58.1448 | 59.7322 |
| 0.0267 | 24.91 | 125 | 2.3389 | 60.4902 | 51.7935 | 57.0689 | 59.7132 |
| 0.0267 | 25.91 | 130 | 2.3168 | 58.8469 | 50.3181 | 55.7386 | 58.3598 |
| 0.0211 | 26.91 | 135 | 2.4147 | 59.4225 | 50.8405 | 56.503 | 58.7221 |
| 0.0211 | 27.91 | 140 | 2.3631 | 59.7489 | 51.2137 | 57.3204 | 59.3348 |
| 0.0211 | 28.91 | 145 | 2.3850 | 60.1718 | 51.4176 | 57.2152 | 59.5157 |
| 0.0211 | 29.91 | 150 | 2.4610 | 60.1433 | 51.433 | 56.6256 | 59.3265 |
| 0.0175 | 30.91 | 155 | 2.4400 | 58.8345 | 49.7031 | 55.3079 | 57.9236 |
| 0.0175 | 31.91 | 160 | 2.4506 | 59.209 | 50.1626 | 55.6451 | 58.5791 |
| 0.0175 | 32.91 | 165 | 2.4316 | 59.7713 | 50.8999 | 56.4235 | 58.9845 |
| 0.0175 | 33.91 | 170 | 2.2781 | 60.1822 | 51.9435 | 57.4586 | 59.6766 |
| 0.0175 | 34.91 | 175 | 2.3849 | 58.2328 | 49.2106 | 55.1516 | 57.5072 |
| 0.0141 | 35.91 | 180 | 2.4872 | 58.4916 | 50.3345 | 55.5991 | 58.1131 |
| 0.0141 | 36.91 | 185 | 2.4883 | 59.0957 | 49.76 | 55.3567 | 58.076 |
| 0.0141 | 37.91 | 190 | 2.4327 | 58.091 | 48.8628 | 54.8678 | 57.5406 |
| 0.0141 | 38.91 | 195 | 2.4998 | 57.7428 | 48.7366 | 54.2166 | 56.7643 |
| 0.0089 | 39.91 | 200 | 2.4107 | 60.1662 | 51.9832 | 57.1372 | 59.6989 |
| 0.0089 | 40.91 | 205 | 2.4700 | 58.2159 | 49.3934 | 54.9265 | 57.4126 |
| 0.0089 | 41.91 | 210 | 2.4833 | 58.7434 | 49.6619 | 55.5239 | 57.9562 |
| 0.0089 | 42.91 | 215 | 2.4703 | 60.2984 | 51.3168 | 56.9082 | 59.3958 |
| 0.0062 | 43.91 | 220 | 2.5306 | 60.5455 | 52.1189 | 57.3213 | 60.0232 |
| 0.0062 | 44.91 | 225 | 2.5181 | 60.2149 | 51.2187 | 56.1935 | 59.3471 |
| 0.0062 | 45.91 | 230 | 2.4871 | 59.8013 | 51.6114 | 56.0911 | 59.0902 |
| 0.0062 | 46.91 | 235 | 2.4811 | 58.0271 | 48.9441 | 54.3108 | 57.3647 |
| 0.0062 | 47.91 | 240 | 2.5290 | 62.5087 | 54.6149 | 59.638 | 62.0455 |
| 0.0072 | 48.91 | 245 | 2.5194 | 58.7193 | 49.9679 | 55.6517 | 58.1569 |
| 0.0072 | 49.91 | 250 | 2.5708 | 58.4626 | 49.5257 | 54.5032 | 58.1413 |
| 0.0072 | 50.91 | 255 | 2.6449 | 58.446 | 49.4625 | 55.1092 | 58.03 |
| 0.0072 | 51.91 | 260 | 2.5592 | 58.859 | 49.4398 | 55.1503 | 57.9663 |
| 0.0056 | 52.91 | 265 | 2.5086 | 59.7322 | 51.3051 | 56.5401 | 59.2726 |
| 0.0056 | 53.91 | 270 | 2.4846 | 57.8603 | 48.2408 | 54.3847 | 57.115 |
| 0.0056 | 54.91 | 275 | 2.5509 | 58.9506 | 50.045 | 55.6658 | 58.3618 |
| 0.0056 | 55.91 | 280 | 2.5032 | 60.2524 | 51.8167 | 56.98 | 59.7506 |
| 0.0056 | 56.91 | 285 | 2.5012 | 60.0596 | 51.4924 | 56.7181 | 59.5037 |
| 0.0054 | 57.91 | 290 | 2.5176 | 61.0622 | 52.6235 | 57.9317 | 60.5036 |
| 0.0054 | 58.91 | 295 | 2.5024 | 62.9246 | 54.8544 | 59.9824 | 62.5584 |
| 0.0054 | 59.91 | 300 | 2.5687 | 62.2602 | 53.9673 | 58.9862 | 61.5837 |
| 0.0054 | 60.91 | 305 | 2.5890 | 62.5706 | 54.227 | 59.2032 | 62.125 |
| 0.0036 | 61.91 | 310 | 2.5454 | 62.1565 | 53.2585 | 58.7169 | 61.3943 |
| 0.0036 | 62.91 | 315 | 2.5629 | 62.8292 | 54.6781 | 59.9889 | 62.254 |
| 0.0036 | 63.91 | 320 | 2.5581 | 58.8394 | 50.4421 | 56.0742 | 58.1945 |
| 0.0036 | 64.91 | 325 | 2.5532 | 59.5814 | 51.1335 | 56.5841 | 59.196 |
| 0.0031 | 65.91 | 330 | 2.5826 | 59.0485 | 50.3992 | 55.5283 | 58.3757 |
| 0.0031 | 66.91 | 335 | 2.5815 | 61.4832 | 52.7977 | 57.7351 | 60.9888 |
| 0.0031 | 67.91 | 340 | 2.5865 | 61.7836 | 53.6797 | 58.6743 | 61.3765 |
| 0.0031 | 68.91 | 345 | 2.6007 | 61.2253 | 52.8781 | 57.7006 | 60.717 |
| 0.0031 | 69.91 | 350 | 2.6210 | 60.717 | 52.4933 | 57.5089 | 60.4196 |
| 0.0035 | 70.91 | 355 | 2.6169 | 61.3491 | 53.3932 | 58.2288 | 60.8793 |
| 0.0035 | 71.91 | 360 | 2.6025 | 62.0101 | 54.0289 | 59.0822 | 61.7202 |
| 0.0035 | 72.91 | 365 | 2.5705 | 61.2227 | 52.9937 | 58.2493 | 60.6631 |
| 0.0035 | 73.91 | 370 | 2.5623 | 59.1718 | 50.7827 | 56.1851 | 58.7118 |
| 0.002 | 74.91 | 375 | 2.5536 | 58.4201 | 49.6923 | 55.0398 | 57.7707 |
| 0.002 | 75.91 | 380 | 2.5478 | 60.2307 | 51.7503 | 57.3173 | 59.692 |
| 0.002 | 76.91 | 385 | 2.6039 | 58.7637 | 49.741 | 55.5341 | 58.0784 |
| 0.002 | 77.91 | 390 | 2.6371 | 59.3929 | 50.6444 | 55.9887 | 58.813 |
| 0.002 | 78.91 | 395 | 2.6238 | 59.0572 | 50.605 | 55.6631 | 58.4366 |
| 0.0019 | 79.91 | 400 | 2.5783 | 57.9852 | 49.2588 | 54.822 | 57.4643 |
| 0.0019 | 80.91 | 405 | 2.5982 | 58.0218 | 49.1651 | 54.9876 | 57.4066 |
| 0.0019 | 81.91 | 410 | 2.6141 | 60.3133 | 51.5723 | 56.9476 | 59.715 |
| 0.0019 | 82.91 | 415 | 2.5904 | 60.8199 | 51.8956 | 58.406 | 60.323 |
| 0.0017 | 83.91 | 420 | 2.5718 | 60.3449 | 51.1433 | 57.6984 | 59.7513 |
| 0.0017 | 84.91 | 425 | 2.5737 | 60.151 | 51.1986 | 57.3376 | 59.378 |
| 0.0017 | 85.91 | 430 | 2.5807 | 60.9273 | 52.2469 | 58.2038 | 60.1642 |
| 0.0017 | 86.91 | 435 | 2.5900 | 60.1846 | 51.6144 | 57.5407 | 59.5109 |
| 0.0011 | 87.91 | 440 | 2.6066 | 62.0776 | 53.6022 | 59.157 | 61.6201 |
| 0.0011 | 88.91 | 445 | 2.6231 | 61.8822 | 53.5232 | 58.965 | 61.401 |
| 0.0011 | 89.91 | 450 | 2.6273 | 60.3358 | 51.9941 | 57.3823 | 59.7729 |
| 0.0011 | 90.91 | 455 | 2.6194 | 60.0196 | 51.6134 | 57.1357 | 59.4594 |
| 0.0011 | 91.91 | 460 | 2.6118 | 60.6898 | 52.1328 | 57.3076 | 60.0351 |
| 0.0015 | 92.91 | 465 | 2.6032 | 61.2119 | 52.5034 | 57.8098 | 60.6634 |
| 0.0015 | 93.91 | 470 | 2.6040 | 61.4812 | 52.8197 | 57.9668 | 60.8767 |
| 0.0015 | 94.91 | 475 | 2.6158 | 61.4046 | 52.8905 | 57.8958 | 60.804 |
| 0.0015 | 95.91 | 480 | 2.6280 | 62.1764 | 53.8521 | 58.8608 | 61.6138 |
| 0.0012 | 96.91 | 485 | 2.6304 | 62.2028 | 53.8967 | 58.8976 | 61.6409 |
| 0.0012 | 97.91 | 490 | 2.6328 | 61.7371 | 53.3908 | 58.4107 | 61.1382 |
| 0.0012 | 98.91 | 495 | 2.6331 | 61.441 | 52.9403 | 58.3426 | 60.8249 |
| 0.0012 | 99.91 | 500 | 2.6315 | 61.441 | 52.9403 | 58.3426 | 60.8249 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.10.3
|
AlekseyKorshuk/bert-finetuned-ner | 58198745f8dd6219a7303702eaa3596570465bab | 2022-03-08T14:27:56.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:wnut_17",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | AlekseyKorshuk | null | AlekseyKorshuk/bert-finetuned-ner | 17 | null | transformers | 9,043 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
model-index:
- name: bert-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wnut_17 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 425 | 0.3961 | 0.5707 | 0.2847 | 0.3799 | 0.9058 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
AmrSheta/Meme | ab6b8aaabee48905907041dc1595f954d9e17b02 | 2022-03-12T20:50:10.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers",
"text-classification"
] | text-classification | false | AmrSheta | null | AmrSheta/Meme | 17 | null | transformers | 9,044 | ---
tags:
- text-classification
---
#meme description classification |
facebook/m2m100-12B-avg-5-ckpt | a8f832018c8e51e3db1652e7ae9652664a1e4647 | 2022-05-26T22:26:32.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"multilingual",
"af",
"am",
"ar",
"ast",
"az",
"ba",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"ceb",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"fa",
"ff",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"ht",
"hu",
"hy",
"id",
"ig",
"ilo",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"lb",
"lg",
"ln",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"ns",
"oc",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"ss",
"su",
"sv",
"sw",
"ta",
"th",
"tl",
"tn",
"tr",
"uk",
"ur",
"uz",
"vi",
"wo",
"xh",
"yi",
"yo",
"zh",
"zu",
"arxiv:2010.11125",
"transformers",
"m2m100-12B",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | facebook | null | facebook/m2m100-12B-avg-5-ckpt | 17 | null | transformers | 9,045 | ---
language:
- multilingual
- af
- am
- ar
- ast
- az
- ba
- be
- bg
- bn
- br
- bs
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- ig
- ilo
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- lb
- lg
- ln
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- no
- ns
- oc
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sd
- si
- sk
- sl
- so
- sq
- sr
- ss
- su
- sv
- sw
- ta
- th
- tl
- tn
- tr
- uk
- ur
- uz
- vi
- wo
- xh
- yi
- yo
- zh
- zu
license: mit
tags:
- m2m100-12B
---
# M2M100 12B (average of last 5 checkpoints)
M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.
It was introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository.
The model that can directly translate between the 9,900 directions of 100 languages.
To translate into a target language, the target language id is forced as the first generated token.
To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method.
*Note: `M2M100Tokenizer` depends on `sentencepiece`, so make sure to install it before running the example.*
To install `sentencepiece` run `pip install sentencepiece`
```python
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
hi_text = "जीवन एक चॉकलेट बॉक्स की तरह है।"
chinese_text = "生活就像一盒巧克力。"
model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100-12B-avg-5-ckpt")
tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100-12B-avg-5-ckpt")
# translate Hindi to French
tokenizer.src_lang = "hi"
encoded_hi = tokenizer(hi_text, return_tensors="pt")
generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.get_lang_id("fr"))
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
# => "La vie est comme une boîte de chocolat."
# translate Chinese to English
tokenizer.src_lang = "zh"
encoded_zh = tokenizer(chinese_text, return_tensors="pt")
generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en"))
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
# => "Life is like a box of chocolate."
```
See the [model hub](https://huggingface.co/models?filter=m2m_100) to look for more fine-tuned versions.
## Languages covered
Afrikaans (af), Amharic (am), Arabic (ar), Asturian (ast), Azerbaijani (az), Bashkir (ba), Belarusian (be), Bulgarian (bg), Bengali (bn), Breton (br), Bosnian (bs), Catalan; Valencian (ca), Cebuano (ceb), Czech (cs), Welsh (cy), Danish (da), German (de), Greeek (el), English (en), Spanish (es), Estonian (et), Persian (fa), Fulah (ff), Finnish (fi), French (fr), Western Frisian (fy), Irish (ga), Gaelic; Scottish Gaelic (gd), Galician (gl), Gujarati (gu), Hausa (ha), Hebrew (he), Hindi (hi), Croatian (hr), Haitian; Haitian Creole (ht), Hungarian (hu), Armenian (hy), Indonesian (id), Igbo (ig), Iloko (ilo), Icelandic (is), Italian (it), Japanese (ja), Javanese (jv), Georgian (ka), Kazakh (kk), Central Khmer (km), Kannada (kn), Korean (ko), Luxembourgish; Letzeburgesch (lb), Ganda (lg), Lingala (ln), Lao (lo), Lithuanian (lt), Latvian (lv), Malagasy (mg), Macedonian (mk), Malayalam (ml), Mongolian (mn), Marathi (mr), Malay (ms), Burmese (my), Nepali (ne), Dutch; Flemish (nl), Norwegian (no), Northern Sotho (ns), Occitan (post 1500) (oc), Oriya (or), Panjabi; Punjabi (pa), Polish (pl), Pushto; Pashto (ps), Portuguese (pt), Romanian; Moldavian; Moldovan (ro), Russian (ru), Sindhi (sd), Sinhala; Sinhalese (si), Slovak (sk), Slovenian (sl), Somali (so), Albanian (sq), Serbian (sr), Swati (ss), Sundanese (su), Swedish (sv), Swahili (sw), Tamil (ta), Thai (th), Tagalog (tl), Tswana (tn), Turkish (tr), Ukrainian (uk), Urdu (ur), Uzbek (uz), Vietnamese (vi), Wolof (wo), Xhosa (xh), Yiddish (yi), Yoruba (yo), Chinese (zh), Zulu (zu)
## BibTeX entry and citation info
```
@misc{fan2020englishcentric,
title={Beyond English-Centric Multilingual Machine Translation},
author={Angela Fan and Shruti Bhosale and Holger Schwenk and Zhiyi Ma and Ahmed El-Kishky and Siddharth Goyal and Mandeep Baines and Onur Celebi and Guillaume Wenzek and Vishrav Chaudhary and Naman Goyal and Tom Birch and Vitaliy Liptchinsky and Sergey Edunov and Edouard Grave and Michael Auli and Armand Joulin},
year={2020},
eprint={2010.11125},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
saattrupdan/job-listing-relevance-model | 3751de206442b9b400d6660d7da787a74aba09c2 | 2022-03-22T19:51:07.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | saattrupdan | null | saattrupdan/job-listing-relevance-model | 17 | null | transformers | 9,046 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: job-listing-relevance-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# job-listing-relevance-model
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: 0.1649
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7435 | 0.43 | 50 | 0.6889 |
| 0.3222 | 0.87 | 100 | 0.2906 |
| 0.2573 | 1.3 | 150 | 0.1937 |
| 0.1205 | 1.74 | 200 | 0.1411 |
| 0.1586 | 2.17 | 250 | 0.2008 |
| 0.0755 | 2.61 | 300 | 0.1926 |
| 0.062 | 3.04 | 350 | 0.2257 |
| 0.0644 | 3.48 | 400 | 0.1497 |
| 0.1034 | 3.91 | 450 | 0.1561 |
| 0.008 | 4.35 | 500 | 0.2067 |
| 0.0616 | 4.78 | 550 | 0.2067 |
| 0.0766 | 5.22 | 600 | 0.1494 |
| 0.0029 | 5.65 | 650 | 0.2078 |
| 0.1076 | 6.09 | 700 | 0.1669 |
| 0.0025 | 6.52 | 750 | 0.1564 |
| 0.0498 | 6.95 | 800 | 0.2355 |
| 0.0011 | 7.39 | 850 | 0.1652 |
| 0.0271 | 7.82 | 900 | 0.1731 |
| 0.012 | 8.26 | 950 | 0.1590 |
| 0.0257 | 8.69 | 1000 | 0.1638 |
| 0.0009 | 9.13 | 1050 | 0.1851 |
| 0.0013 | 9.56 | 1100 | 0.1613 |
| 0.0015 | 10.0 | 1150 | 0.1649 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
RomanEnikeev/distilbert-base-uncased-finetuned-cola | f8049e8669ceb20d8a2282e612b3229840074d7a | 2022-03-25T09:13:46.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | RomanEnikeev | null | RomanEnikeev/distilbert-base-uncased-finetuned-cola | 17 | 0 | transformers | 9,047 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5670814703238499
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8265
- Matthews Correlation: 0.5671
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5216 | 1.0 | 535 | 0.5536 | 0.4041 |
| 0.3481 | 2.0 | 1070 | 0.5242 | 0.5206 |
| 0.2372 | 3.0 | 1605 | 0.6162 | 0.5311 |
| 0.1701 | 4.0 | 2140 | 0.7704 | 0.5461 |
| 0.1304 | 5.0 | 2675 | 0.8265 | 0.5671 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
l3cube-pune/hing-mbert-mixed | 865aa54a29dbb68d074172807e17dda68dc7ecde | 2022-06-26T15:12:05.000Z | [
"pytorch",
"bert",
"fill-mask",
"hi",
"en",
"dataset:L3Cube-HingCorpus",
"arxiv:2204.08398",
"transformers",
"codemix",
"license:cc-by-4.0",
"autotrain_compatible"
] | fill-mask | false | l3cube-pune | null | l3cube-pune/hing-mbert-mixed | 17 | null | transformers | 9,048 | ---
license: cc-by-4.0
language:
- hi
- en
tags:
- hi
- en
- codemix
datasets:
- L3Cube-HingCorpus
---
## HingBERT-Mixed
HingBERT-Mixed is a Hindi-English code-mixed BERT model trained on roman + devanagari text. It is a base BERT model fine-tuned on mixed script L3Cube-HingCorpus.
<br>
[dataset link] (https://github.com/l3cube-pune/code-mixed-nlp)
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2204.08398)
```
@InProceedings{nayak-joshi:2022:WILDRE6,
author = {Nayak, Ravindra and Joshi, Raviraj},
title = {L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models},
booktitle = {Proceedings of The WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {7--12}
}
``` |
Graphcore/lxmert-gqa-uncased | 7827f5b7093dd9ef2119df8ab3a512526cdffe68 | 2022-05-25T18:28:12.000Z | [
"pytorch",
"lxmert",
"question-answering",
"dataset:Graphcore/gqa-lxmert",
"arxiv:1908.07490",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | Graphcore | null | Graphcore/lxmert-gqa-uncased | 17 | null | transformers | 9,049 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- Graphcore/gqa-lxmert
metrics:
- accuracy
model-index:
- name: gqa
results:
- task:
name: Question Answering
type: question-answering
dataset:
name: Graphcore/gqa-lxmert
type: Graphcore/gqa-lxmert
args: gqa
metrics:
- name: Accuracy
type: accuracy
value: 0.5933514030612245
---
<!-- 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. -->
# Graphcore/lxmert-gqa-uncased
BERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabeled texts. It enables easy and fast fine-tuning for different downstream task such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM.
It was trained with two objectives in pretraining : Masked language modeling(MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation. In addition to MLM, NSP is used for jointly pertaining text-pair representations.
It reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks.
## Model description
LXMERT is a transformer model for learning vision-and-language cross-modality representations. It has a Transformer model that has three encoders: object relationship encoder, a language encoder, and a cross-modality encoder. It is pretrained via a combination of masked language modelling, visual-language text alignment, ROI-feature regression, masked visual-attribute modeling, masked visual-object modelling, and visual-question answering objectives. It achieves the state-of-the-art results on VQA anad GQA.
Paper link : [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://arxiv.org/pdf/1908.07490.pdf)
## Intended uses & limitations
This model is a fine-tuned version of [unc-nlp/lxmert-base-uncased](https://huggingface.co/unc-nlp/lxmert-base-uncased) on the [Graphcore/gqa-lxmert](https://huggingface.co/datasets/Graphcore/gqa-lxmert) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9326
- Accuracy: 0.5934
## Training and evaluation data
- [Graphcore/gqa-lxmert](https://huggingface.co/datasets/Graphcore/gqa-lxmert) dataset
## Training procedure
Trained on 16 Graphcore Mk2 IPUs using [optimum-graphcore](https://github.com/huggingface/optimum-graphcore).
Command line:
```
python examples/question-answering/run_vqa.py \
--model_name_or_path unc-nlp/lxmert-base-uncased \
--ipu_config_name Graphcore/lxmert-base-ipu \
--dataset_name Graphcore/gqa-lxmert \
--do_train \
--do_eval \
--max_seq_length 512 \
--per_device_train_batch_size 1 \
--num_train_epochs 4 \
--dataloader_num_workers 64 \
--logging_steps 5 \
--learning_rate 1e-5 \
--lr_scheduler_type linear \
--loss_scaling 16384 \
--weight_decay 0.01 \
--warmup_ratio 0.1 \
--output_dir /tmp/gqa/ \
--dataloader_drop_last \
--replace_qa_head \
--pod_type pod16
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: IPU
- total_train_batch_size: 64
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0
- training precision: Mixed Precision
### Training results
```
***** train metrics *****
"epoch": 4.0,
"train_loss": 0.6123406731570221,
"train_runtime": 29986.2288,
"train_samples": 943000,
"train_samples_per_second": 125.791,
"train_steps_per_second": 1.965
***** eval metrics *****
"eval_accuracy": 0.5933514030612245,
"eval_loss": 1.9326171875,
"eval_samples": 12576,
```
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6
|
IIC/roberta-base-bne-ranker | 8ee5133c03047e93559dfbfd6f2122045e91e8c3 | 2022-04-02T15:04:54.000Z | [
"pytorch",
"roberta",
"text-classification",
"es",
"dataset:IIC/msmarco_es",
"transformers",
"sentence similarity",
"passage reranking",
"model-index"
] | text-classification | false | IIC | null | IIC/roberta-base-bne-ranker | 17 | null | transformers | 9,050 | ---
language:
- es
tags:
- sentence similarity # Example: audio
- passage reranking # Example: automatic-speech-recognition
datasets:
- IIC/msmarco_es
metrics:
- eval_MRR@10: 0.688
model-index:
- name: roberta-base-bne-ranker
results:
- task:
type: text similarity # Required. Example: automatic-speech-recognition
name: text similarity # Optional. Example: Speech Recognition
dataset:
type: IIC/msmarco_es # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: IIC/msmarco_es # Required. Example: Common Voice zh-CN
args: es # Optional. Example: zh-CN
metrics:
- type: MRR@10
value: 0.688
name: eval_MRR@10
---
This is a model to rank documents based on importance. It is trained on an [automatically translated version of MS Marco](https://huggingface.co/datasets/IIC/msmarco_es). After some experiments, the best configuration was to train for 2 epochs with learning rate 2e-5 and batch size 32.
Example of use:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder("IIC/roberta-base-bne-ranker", device="cpu")
question = "¿Cómo se llama el rey?"
contexts = ["Me encanta la canción de el rey", "Cuando el rey fue a Sevilla, perdió su silla", "El rey se llama Juan Carlos y es conocido por sus escándalos"]
similarity_scores = model.predict([[question, context] for context in contexts])
```
### Contributions
Thanks to [@avacaondata](https://huggingface.co/avacaondata), [@alborotis](https://huggingface.co/alborotis), [@albarji](https://huggingface.co/albarji), [@Dabs](https://huggingface.co/Dabs), [@GuillemGSubies](https://huggingface.co/GuillemGSubies) for adding this model. |
Meowren/MichaelScottBott | d48033535b3d403e3a55b76c3323f38588441195 | 2022-05-16T16:03:13.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Meowren | null | Meowren/MichaelScottBott | 17 | null | transformers | 9,051 | ---
tags:
- conversational
---
# Michael Scott DialoGPT Model
|
nielsr/convnext-tiny-finetuned-eurostat | f836aee3c8bc4e7424702ed00d2b8343bd0dbf21 | 2022-04-04T19:25:58.000Z | [
"pytorch",
"convnext",
"image-classification",
"dataset:eurosat",
"transformers",
"license:apache-2.0"
] | image-classification | false | nielsr | null | nielsr/convnext-tiny-finetuned-eurostat | 17 | null | transformers | 9,052 | ---
license: apache-2.0
datasets:
- eurosat
widget:
- src: forest.png
example_title: Forest
---
# ConvNext fine-tuned on Eurosat
This model is a `facebook/convnext-tiny-224` model fine-tuned on the [Eurosat dataset](https://github.com/phelber/EuroSAT). |
Intel/bert-base-uncased-mrpc-int8-qat | 54b72a05d03d7085c951b861c3d546cfe5de354a | 2022-06-10T02:43:22.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:mrpc",
"transformers",
"text-classfication",
"int8",
"Intel® Neural Compressor",
"QuantizationAwareTraining",
"license:apache-2.0"
] | text-classification | false | Intel | null | Intel/bert-base-uncased-mrpc-int8-qat | 17 | null | transformers | 9,053 | ---
language: en
license: apache-2.0
tags:
- text-classfication
- int8
- Intel® Neural Compressor
- QuantizationAwareTraining
datasets:
- mrpc
metrics:
- f1
---
# INT8 BERT base uncased finetuned MRPC
### QuantizationAwareTraining
This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model comes from the fine-tuned model [Intel/bert-base-uncased-mrpc](https://huggingface.co/Intel/bert-base-uncased-mrpc).
### Test result
| |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |0.9142|0.9042|
| **Model size (MB)** |107|418|
### Load with Intel® Neural Compressor:
```python
from neural_compressor.utils.load_huggingface import OptimizedModel
int8_model = OptimizedModel.from_pretrained(
'Intel/bert-base-uncased-mrpc-int8-qat',
)
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
- train_batch_size: 8
- eval_batch_size: 8
- eval_steps: 100
- load_best_model_at_end: True
- metric_for_best_model: f1
- early_stopping_patience = 6
- early_stopping_threshold = 0.001
|
Stremie/bert-base-uncased-clickbait-keywords | a629da66d459d9ced721b258d5f5ca5f5cad1db1 | 2022-04-18T12:49:08.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Stremie | null | Stremie/bert-base-uncased-clickbait-keywords | 17 | null | transformers | 9,054 | This model classifies whether a tweet is clickbait or not. It has been trained using [Webis-Clickbait-17](https://webis.de/data/webis-clickbait-17.html) dataset. Input is composed of 'postText' + '[SEP]' + 'targetKeywords'. Achieved ~0.7 F1-score on test data. |
Kuray107/librispeech-100h-supervised-meta | 263762b247ca3d1590e5d0f257fac9ea3b7bb836 | 2022-04-11T14:24:58.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Kuray107 | null | Kuray107/librispeech-100h-supervised-meta | 17 | null | transformers | 9,055 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: librispeech-100h-supervised-meta
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. -->
# librispeech-100h-supervised-meta
This model is a fine-tuned version of [Kuray107/librispeech-5h-supervised](https://huggingface.co/Kuray107/librispeech-5h-supervised) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0965
- Wer: 0.0330
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.1131 | 1.12 | 1000 | 0.0755 | 0.0487 |
| 0.0725 | 2.24 | 2000 | 0.0637 | 0.0404 |
| 0.0539 | 3.36 | 3000 | 0.0661 | 0.0389 |
| 0.0441 | 4.48 | 4000 | 0.0637 | 0.0371 |
| 0.0379 | 5.61 | 5000 | 0.0675 | 0.0356 |
| 0.0341 | 6.73 | 6000 | 0.0735 | 0.0360 |
| 0.0295 | 7.85 | 7000 | 0.0737 | 0.0362 |
| 0.0265 | 8.97 | 8000 | 0.0741 | 0.0350 |
| 0.0244 | 10.09 | 9000 | 0.0779 | 0.0337 |
| 0.0217 | 11.21 | 10000 | 0.0835 | 0.0343 |
| 0.0203 | 12.33 | 11000 | 0.0785 | 0.0339 |
| 0.0188 | 13.45 | 12000 | 0.0827 | 0.0344 |
| 0.0179 | 14.57 | 13000 | 0.0875 | 0.0332 |
| 0.0169 | 15.7 | 14000 | 0.0860 | 0.0330 |
| 0.0158 | 16.82 | 15000 | 0.0954 | 0.0330 |
| 0.0147 | 17.94 | 16000 | 0.0934 | 0.0329 |
| 0.0148 | 19.06 | 17000 | 0.0965 | 0.0330 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Conrad747/lg-en | 49f97fb3cc1b52693783027c6a3d44f14288d83e | 2022-07-20T13:39:31.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | Conrad747 | null | Conrad747/lg-en | 17 | null | transformers | 9,056 | ---
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: lg-en
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. -->
# lg-en
This model is a fine-tuned version of [AI-Lab-Makerere/lg_en](https://huggingface.co/AI-Lab-Makerere/lg_en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0047
- Bleu: 31.3411
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 1.0 | 178 | 1.0047 | 31.3411 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
amir36/distilbert-base-uncased-finetuned-emotion | d721f69df9829e53438617352c3f33e8e6313068 | 2022-07-14T02:52:28.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | amir36 | null | amir36/distilbert-base-uncased-finetuned-emotion | 17 | null | transformers | 9,057 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.921
- name: F1
type: f1
value: 0.920970510317642
---
<!-- 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: 0.2180
- Accuracy: 0.921
- F1: 0.9210
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8133 | 1.0 | 250 | 0.3078 | 0.9095 | 0.9076 |
| 0.2431 | 2.0 | 500 | 0.2180 | 0.921 | 0.9210 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.4
- Tokenizers 0.11.6
|
studio-ousia/luke-large-lite | 367bdf0609d247be6ce1eb76f9f228d40d26d05a | 2022-04-13T10:32:20.000Z | [
"pytorch",
"luke",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | studio-ousia | null | studio-ousia/luke-large-lite | 17 | null | transformers | 9,058 | Entry not found |
Toshifumi/distilbert-base-multilingual-cased-finetuned-emotion | c44daf307230625367378c08e353508ae3f29a16 | 2022-04-13T12:30:50.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | Toshifumi | null | Toshifumi/distilbert-base-multilingual-cased-finetuned-emotion | 17 | null | transformers | 9,059 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-multilingual-cased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8885
- name: F1
type: f1
value: 0.8888307905223247
---
<!-- 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-multilingual-cased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3702
- Accuracy: 0.8885
- F1: 0.8888
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.1646 | 1.0 | 250 | 0.6190 | 0.8085 | 0.7992 |
| 0.4536 | 2.0 | 500 | 0.3702 | 0.8885 | 0.8888 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
rmihaylov/bert-base-pos-theseus-bg | e85ab91f3bc5524d7e491d17883feb065203b2f8 | 2022-04-16T19:26:17.000Z | [
"pytorch",
"bert",
"token-classification",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"arxiv:1810.04805",
"arxiv:2002.02925",
"transformers",
"torch",
"license:mit",
"autotrain_compatible"
] | token-classification | false | rmihaylov | null | rmihaylov/bert-base-pos-theseus-bg | 17 | null | transformers | 9,060 | ---
inference: false
language:
- bg
license: mit
datasets:
- oscar
- chitanka
- wikipedia
tags:
- torch
---
# BERT BASE (cased) finetuned on Bulgarian part-of-speech data
Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is cased: it does make a difference
between bulgarian and Bulgarian. The training data is Bulgarian text from [OSCAR](https://oscar-corpus.com/post/oscar-2019/), [Chitanka](https://chitanka.info/) and [Wikipedia](https://bg.wikipedia.org/).
It was finetuned on public part-of-speech Bulgarian data.
Then, it was compressed via [progressive module replacing](https://arxiv.org/abs/2002.02925).
### How to use
Here is how to use this model in PyTorch:
```python
>>> from transformers import pipeline
>>>
>>> model = pipeline(
>>> 'token-classification',
>>> model='rmihaylov/bert-base-pos-theseus-bg',
>>> tokenizer='rmihaylov/bert-base-pos-theseus-bg',
>>> device=0,
>>> revision=None)
>>> output = model('Здравей, аз се казвам Иван.')
>>> print(output)
[{'end': 7,
'entity': 'INTJ',
'index': 1,
'score': 0.9640711,
'start': 0,
'word': '▁Здравей'},
{'end': 8,
'entity': 'PUNCT',
'index': 2,
'score': 0.9998927,
'start': 7,
'word': ','},
{'end': 11,
'entity': 'PRON',
'index': 3,
'score': 0.9998872,
'start': 8,
'word': '▁аз'},
{'end': 14,
'entity': 'PRON',
'index': 4,
'score': 0.99990034,
'start': 11,
'word': '▁се'},
{'end': 21,
'entity': 'VERB',
'index': 5,
'score': 0.99989736,
'start': 14,
'word': '▁казвам'},
{'end': 26,
'entity': 'PROPN',
'index': 6,
'score': 0.99990785,
'start': 21,
'word': '▁Иван'},
{'end': 27,
'entity': 'PUNCT',
'index': 7,
'score': 0.9999685,
'start': 26,
'word': '.'}]
```
|
ToToKr/kobigbird-bert-base-finetuned-klue | 518fbcf145fdcc835d00a37a895bd7b0282b1cf5 | 2022-06-07T08:24:06.000Z | [
"pytorch",
"tensorboard",
"big_bird",
"question-answering",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | ToToKr | null | ToToKr/kobigbird-bert-base-finetuned-klue | 17 | null | transformers | 9,061 | ---
tags:
- generated_from_trainer
model-index:
- name: kobigbird-bert-base-finetuned-klue
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. -->
# kobigbird-bert-base-finetuned-klue
This model is a fine-tuned version of [monologg/kobigbird-bert-base](https://huggingface.co/monologg/kobigbird-bert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8347
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 5.3957 | 0.13 | 500 | 3.7603 |
| 3.2242 | 0.26 | 1000 | 2.3961 |
| 2.0812 | 0.4 | 1500 | 1.5552 |
| 1.6198 | 0.53 | 2000 | 1.3609 |
| 1.447 | 0.66 | 2500 | 1.2270 |
| 1.3438 | 0.79 | 3000 | 1.1321 |
| 1.2399 | 0.93 | 3500 | 1.0973 |
| 1.1976 | 1.06 | 4000 | 1.0418 |
| 1.1177 | 1.19 | 4500 | 1.0301 |
| 1.0811 | 1.32 | 5000 | 1.0232 |
| 1.0506 | 1.45 | 5500 | 0.9971 |
| 1.0293 | 1.59 | 6000 | 0.9580 |
| 1.0196 | 1.72 | 6500 | 0.9551 |
| 0.9846 | 1.85 | 7000 | 0.9274 |
| 0.9702 | 1.98 | 7500 | 0.9286 |
| 0.9224 | 2.11 | 8000 | 0.8961 |
| 0.8867 | 2.25 | 8500 | 0.9193 |
| 0.8711 | 2.38 | 9000 | 0.8727 |
| 0.883 | 2.51 | 9500 | 0.8790 |
| 0.8513 | 2.64 | 10000 | 0.8830 |
| 0.8709 | 2.78 | 10500 | 0.8604 |
| 0.8766 | 2.91 | 11000 | 0.8260 |
| 0.7976 | 3.04 | 11500 | 0.8401 |
| 0.7724 | 3.17 | 12000 | 0.8617 |
| 0.78 | 3.3 | 12500 | 0.8601 |
| 0.7566 | 3.44 | 13000 | 0.8657 |
| 0.7407 | 3.57 | 13500 | 0.8347 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
AJGP/bert-finetuned-ner | d7b33d9a94cbae6b6a6c910649e7bd30ccebd4ec | 2022-04-17T14:57:27.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | AJGP | null | AJGP/bert-finetuned-ner | 17 | null | transformers | 9,062 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9354518371400199
- name: Recall
type: recall
value: 0.9511948838774823
- name: F1
type: f1
value: 0.9432576769025368
- name: Accuracy
type: accuracy
value: 0.9868870312591982
---
<!-- 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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0598
- Precision: 0.9355
- Recall: 0.9512
- F1: 0.9433
- Accuracy: 0.9869
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0833 | 1.0 | 1756 | 0.0654 | 0.9202 | 0.9350 | 0.9275 | 0.9833 |
| 0.034 | 2.0 | 3512 | 0.0610 | 0.9262 | 0.9458 | 0.9359 | 0.9846 |
| 0.0233 | 3.0 | 5268 | 0.0598 | 0.9355 | 0.9512 | 0.9433 | 0.9869 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
mwong/albert-base-fever-claim-related | 4848442a348fedbb771c97df962650c0644884c4 | 2022-06-24T03:34:53.000Z | [
"pytorch",
"albert",
"text-classification",
"en",
"dataset:mwong/fever-claim-related",
"transformers",
"text classification",
"fact checking",
"license:mit"
] | text-classification | false | mwong | null | mwong/albert-base-fever-claim-related | 17 | 1 | transformers | 9,063 | ---
language: en
license: mit
tags:
- text classification
- fact checking
datasets:
- mwong/fever-claim-related
widget:
- text: "Earth’s changing climate is a critical issue and poses the risk of significant environmental, social and economic disruptions around the globe.</s></s>Because of fears of climate change and adverse effects of drilling explosions and oil spills in the Gulf of Mexico, legislation has been considered, and governmental regulations and orders have been issued, which, combined with the local economic and employment conditions caused by both, could materially adversely impact the oil and gas industries and the economic health of areas in which a significant number of our stores are located."
example_title: "Evidence related to claim"
metrics: f1
---
# FeverAlbert
FeverAlbert is a classifier model that predicts if evidence is related to query claim. The model achieved F1 score of 88.33% with test dataset "mwong/fever-claim-related". Using pretrained albert-base-v2 model, the classifier head is trained on Fever dataset. |
Intel/bert-base-uncased-mrpc-int8-dynamic | eab02b076b47301343cb77fa7cf23d029bee7376 | 2022-06-10T02:32:38.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:mrpc",
"transformers",
"text-classfication",
"int8",
"Intel® Neural Compressor",
"PostTrainingDynamic",
"license:apache-2.0"
] | text-classification | false | Intel | null | Intel/bert-base-uncased-mrpc-int8-dynamic | 17 | null | transformers | 9,064 | ---
language: en
license: apache-2.0
tags:
- text-classfication
- int8
- Intel® Neural Compressor
- PostTrainingDynamic
datasets:
- mrpc
metrics:
- f1
---
# INT8 BERT base uncased finetuned MRPC
### Post-training dynamic quantization
This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model comes from the fine-tuned model [Intel/bert-base-uncased-mrpc](https://huggingface.co/Intel/bert-base-uncased-mrpc).
### Test result
| |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |0.8997|0.9042|
| **Model size (MB)** |174|418|
### Load with Intel® Neural Compressor:
```python
from neural_compressor.utils.load_huggingface import OptimizedModel
int8_model = OptimizedModel.from_pretrained(
'Intel/bert-base-uncased-mrpc-int8-dynamic',
)
```
|
Hate-speech-CNERG/tamil-codemixed-abusive-MuRIL | 6eef32cd2cd8eb9f26dd76beaeec370ab6c48b2f | 2022-05-03T08:52:47.000Z | [
"pytorch",
"bert",
"text-classification",
"ta-en",
"arxiv:2204.12543",
"transformers",
"license:afl-3.0"
] | text-classification | false | Hate-speech-CNERG | null | Hate-speech-CNERG/tamil-codemixed-abusive-MuRIL | 17 | null | transformers | 9,065 | ---
language: ta-en
license: afl-3.0
---
This model is used to detect **abusive speech** in **Code-Mixed Tamil**. It is finetuned on MuRIL model using Code-Mixed Tamil abusive speech dataset.
The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive)
LABEL_0 :-> Normal
LABEL_1 :-> Abusive
### For more details about our paper
Mithun Das, Somnath Banerjee and Animesh Mukherjee. "[Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://arxiv.org/abs/2204.12543)". Accepted at ACM HT 2022.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{das2022data,
title={Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages},
author={Das, Mithun and Banerjee, Somnath and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2204.12543},
year={2022}
}
~~~ |
benjamin/gpt2-wechsel-ukrainian | b654dd26f575dc9d2ff07bf501e5c442b22d5e39 | 2022-04-29T17:42:44.000Z | [
"pytorch",
"gpt2",
"text-generation",
"uk",
"arxiv:2112.06598",
"transformers",
"license:mit"
] | text-generation | false | benjamin | null | benjamin/gpt2-wechsel-ukrainian | 17 | 1 | transformers | 9,066 | ---
license: mit
language: uk
---
# gpt2-wechsel-ukrainian
[`gpt2`](https://huggingface.co/gpt2) transferred to Ukrainian using the method from the NAACL2022 paper [WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models](https://arxiv.org/abs/2112.065989). |
KoenBronstring/finetuning-sentiment-model-3000-samples | ae2500fe723ee0c8ac6856d16e7815bbfda2e57e | 2022-05-04T17:53:58.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | KoenBronstring | null | KoenBronstring/finetuning-sentiment-model-3000-samples | 17 | null | transformers | 9,067 | ---
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
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8733333333333333
- name: F1
type: f1
value: 0.8758169934640523
---
<!-- 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: 0.3149
- Accuracy: 0.8733
- F1: 0.8758
## 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 4.18.0
- Pytorch 1.11.0+cpu
- Datasets 2.1.0
- Tokenizers 0.12.1
|
mikeadimech/pegasus-qmsum-meeting-summarization | 1c8b4f4ac589d791c6f976cce4d05e945ee84cb9 | 2022-05-25T16:15:41.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"dataset:yawnick/QMSum",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | mikeadimech | null | mikeadimech/pegasus-qmsum-meeting-summarization | 17 | null | transformers | 9,068 | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pegasus-qmsum-meeting-summarization
results: []
datasets:
- yawnick/QMSum
---
<!-- 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. -->
# pegasus-qmsum-meeting-summarization
This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on the QMSum dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2331
- Rouge1: 32.7156
- Rouge2: 10.5699
- Rougel: 23.2759
- Rougelsum: 29.7903
- Gen Len: 61.65
## 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-06
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 300
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:------:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 5.5746 | 1.09 | 100 | 5.1739 | 9.4941 | 1.7868 | 7.2455 | 8.4302 | 29.825 |
| 5.5784 | 2.17 | 200 | 5.0939 | 9.113 | 1.7887 | 6.9741 | 8.0457 | 26.85 |
| 5.3777 | 3.26 | 300 | 4.9723 | 9.6387 | 1.9301 | 7.349 | 8.7941 | 25.325 |
| 5.1884 | 4.35 | 400 | 4.8423 | 10.6045 | 2.4008 | 7.8423 | 9.4593 | 22.625 |
| 5.0795 | 5.43 | 500 | 4.7313 | 13.7621 | 3.1231 | 9.6944 | 12.2204 | 32.175 |
| 4.9369 | 6.52 | 600 | 4.6555 | 19.5696 | 4.9121 | 14.2603 | 16.9622 | 46.45 |
| 4.8926 | 7.61 | 700 | 4.6038 | 22.8411 | 5.9791 | 17.2227 | 20.1173 | 51.825 |
| 4.7502 | 8.7 | 800 | 4.5659 | 24.0555 | 6.1971 | 18.967 | 20.9143 | 54.25 |
| 4.6876 | 9.78 | 900 | 4.5379 | 24.7066 | 6.0317 | 19.542 | 21.5774 | 57.575 |
| 4.6266 | 10.87 | 1000 | 4.5160 | 26.128 | 6.5089 | 20.5573 | 22.5338 | 58.0 |
| 4.6303 | 11.96 | 1100 | 4.4983 | 26.6639 | 7.1208 | 20.5222 | 23.5783 | 57.925 |
| 4.6263 | 13.04 | 1200 | 4.4815 | 26.8262 | 7.1029 | 20.5172 | 23.6216 | 57.575 |
| 4.577 | 14.13 | 1300 | 4.4667 | 27.7952 | 7.8331 | 21.1111 | 24.6086 | 56.95 |
| 4.5797 | 15.22 | 1400 | 4.4559 | 27.728 | 7.8144 | 21.1519 | 24.4858 | 56.6 |
| 4.4923 | 16.3 | 1500 | 4.4448 | 28.0998 | 8.1346 | 21.4004 | 25.3769 | 55.975 |
| 4.4583 | 17.39 | 1600 | 4.4335 | 28.9003 | 8.6135 | 22.0139 | 26.0409 | 56.55 |
| 4.5036 | 18.48 | 1700 | 4.4246 | 29.2187 | 8.8301 | 22.3569 | 26.1964 | 58.125 |
| 4.4383 | 19.57 | 1800 | 4.4144 | 28.8424 | 8.9131 | 22.0398 | 25.9214 | 56.75 |
| 4.4797 | 20.65 | 1900 | 4.4054 | 28.9285 | 8.9298 | 22.222 | 26.0316 | 56.225 |
| 4.4264 | 21.74 | 2000 | 4.3989 | 29.7184 | 9.0477 | 22.2885 | 26.7439 | 56.225 |
| 4.3615 | 22.83 | 2100 | 4.3902 | 29.1538 | 8.9529 | 22.0076 | 26.4925 | 57.175 |
| 4.329 | 23.91 | 2200 | 4.3839 | 29.5186 | 9.2777 | 21.9025 | 26.3141 | 55.5 |
| 4.3578 | 25.0 | 2300 | 4.3766 | 28.4309 | 8.9423 | 21.0945 | 25.8191 | 53.975 |
| 4.3748 | 26.09 | 2400 | 4.3707 | 28.3 | 9.0625 | 21.4946 | 25.1966 | 53.0 |
| 4.3233 | 27.17 | 2500 | 4.3639 | 28.2325 | 8.9889 | 21.6226 | 25.3677 | 54.6 |
| 4.339 | 28.26 | 2600 | 4.3578 | 28.0744 | 8.774 | 21.2509 | 25.2901 | 54.1 |
| 4.2798 | 29.35 | 2700 | 4.3532 | 27.772 | 8.7096 | 21.1687 | 25.3345 | 54.025 |
| 4.2964 | 30.43 | 2800 | 4.3465 | 27.7827 | 8.1597 | 20.8139 | 25.0152 | 54.45 |
| 4.3365 | 31.52 | 2900 | 4.3423 | 28.2039 | 8.4661 | 21.3546 | 25.6381 | 55.5 |
| 4.2385 | 32.61 | 3000 | 4.3380 | 28.1098 | 8.6483 | 21.5279 | 25.2009 | 53.95 |
| 4.2451 | 33.7 | 3100 | 4.3331 | 28.2745 | 8.5024 | 21.4456 | 25.3363 | 52.6 |
| 4.2393 | 34.78 | 3200 | 4.3289 | 28.7597 | 9.0881 | 21.6532 | 25.8954 | 52.65 |
| 4.2116 | 35.87 | 3300 | 4.3252 | 29.0463 | 9.1218 | 21.8026 | 26.2037 | 53.65 |
| 4.2175 | 36.96 | 3400 | 4.3210 | 28.8009 | 9.0188 | 21.8368 | 25.8678 | 52.85 |
| 4.2071 | 38.04 | 3500 | 4.3169 | 28.9313 | 8.9787 | 21.3554 | 26.0628 | 54.325 |
| 4.1775 | 39.13 | 3600 | 4.3132 | 28.837 | 8.9621 | 21.6342 | 26.0569 | 54.025 |
| 4.1962 | 40.22 | 3700 | 4.3086 | 28.9265 | 9.0701 | 21.588 | 26.0702 | 53.075 |
| 4.1452 | 41.3 | 3800 | 4.3060 | 29.7968 | 9.366 | 22.1712 | 26.8461 | 54.925 |
| 4.1912 | 42.39 | 3900 | 4.3018 | 29.1488 | 9.1631 | 21.6566 | 26.1476 | 54.25 |
| 4.1356 | 43.48 | 4000 | 4.2984 | 30.0138 | 9.2456 | 22.2547 | 27.2714 | 54.85 |
| 4.1272 | 44.57 | 4100 | 4.2949 | 29.8858 | 9.1498 | 22.1221 | 27.0798 | 55.65 |
| 4.1174 | 45.65 | 4200 | 4.2895 | 30.0427 | 9.2297 | 22.2602 | 27.4219 | 56.175 |
| 4.1029 | 46.74 | 4300 | 4.2885 | 29.9443 | 9.4293 | 22.1229 | 27.3496 | 56.45 |
| 4.157 | 47.83 | 4400 | 4.2851 | 30.3693 | 9.406 | 22.471 | 27.7511 | 56.775 |
| 4.1105 | 48.91 | 4500 | 4.2827 | 30.6193 | 9.7082 | 22.6169 | 27.8044 | 57.225 |
| 4.083 | 50.0 | 4600 | 4.2796 | 30.8083 | 9.9211 | 22.5228 | 28.1236 | 57.575 |
| 4.0891 | 51.09 | 4700 | 4.2764 | 30.4201 | 9.6192 | 22.4747 | 27.7514 | 57.475 |
| 4.0603 | 52.17 | 4800 | 4.2741 | 30.7777 | 9.7432 | 22.6705 | 27.5956 | 57.1 |
| 4.0472 | 53.26 | 4900 | 4.2731 | 30.8093 | 9.7916 | 22.5533 | 27.7858 | 56.15 |
| 4.0712 | 54.35 | 5000 | 4.2703 | 29.9667 | 9.5645 | 22.113 | 26.647 | 56.525 |
| 4.0658 | 55.43 | 5100 | 4.2674 | 29.5415 | 9.4291 | 21.6862 | 26.7816 | 56.55 |
| 4.059 | 56.52 | 5200 | 4.2659 | 30.2032 | 9.8875 | 22.2539 | 27.1801 | 56.925 |
| 4.0257 | 57.61 | 5300 | 4.2629 | 30.3181 | 9.8187 | 22.4266 | 27.4318 | 56.925 |
| 4.0002 | 58.7 | 5400 | 4.2608 | 29.6641 | 9.9252 | 22.1725 | 27.0764 | 56.6 |
| 4.0978 | 59.78 | 5500 | 4.2591 | 30.653 | 10.087 | 22.6956 | 27.7481 | 56.25 |
| 3.9978 | 60.87 | 5600 | 4.2568 | 29.5473 | 9.5653 | 21.6367 | 26.391 | 55.825 |
| 3.9832 | 61.96 | 5700 | 4.2552 | 30.6368 | 10.1624 | 22.7204 | 27.5866 | 57.425 |
| 3.9841 | 63.04 | 5800 | 4.2525 | 30.3045 | 9.7966 | 22.2939 | 27.0978 | 57.725 |
| 4.002 | 64.13 | 5900 | 4.2507 | 30.4468 | 9.9323 | 22.6572 | 27.0761 | 57.5 |
| 3.9705 | 65.22 | 6000 | 4.2491 | 30.1218 | 9.6921 | 22.465 | 26.3835 | 57.55 |
| 3.9863 | 66.3 | 6100 | 4.2477 | 31.3982 | 9.9901 | 22.8762 | 27.6169 | 58.975 |
| 3.9308 | 67.39 | 6200 | 4.2454 | 30.2673 | 9.5804 | 22.4474 | 26.6111 | 59.2 |
| 3.9794 | 68.48 | 6300 | 4.2449 | 30.8612 | 9.8254 | 22.8444 | 27.4979 | 58.075 |
| 3.9499 | 69.57 | 6400 | 4.2412 | 30.8366 | 9.7 | 22.4469 | 27.1621 | 59.025 |
| 3.9722 | 70.65 | 6500 | 4.2414 | 30.9625 | 9.8251 | 22.4089 | 27.4342 | 59.1 |
| 3.9125 | 71.74 | 6600 | 4.2394 | 30.5777 | 9.5514 | 22.1581 | 26.8665 | 58.75 |
| 3.9184 | 72.83 | 6700 | 4.2396 | 30.8306 | 9.5469 | 22.6571 | 27.4302 | 59.725 |
| 3.9337 | 73.91 | 6800 | 4.2377 | 30.8688 | 9.6733 | 22.3073 | 27.2943 | 58.975 |
| 3.9145 | 75.0 | 6900 | 4.2358 | 30.467 | 9.6393 | 22.225 | 27.0127 | 58.45 |
| 3.9038 | 76.09 | 7000 | 4.2353 | 30.6344 | 9.3676 | 22.1945 | 27.1871 | 59.275 |
| 3.893 | 77.17 | 7100 | 4.2335 | 31.4486 | 9.8839 | 22.735 | 27.7854 | 59.025 |
| 3.885 | 78.26 | 7200 | 4.2318 | 30.7118 | 9.8568 | 22.2546 | 27.3983 | 58.5 |
| 3.9266 | 79.35 | 7300 | 4.2304 | 31.6171 | 9.8817 | 22.6145 | 27.6888 | 59.25 |
| 3.8826 | 80.43 | 7400 | 4.2299 | 31.0976 | 9.4662 | 22.2285 | 27.817 | 58.95 |
| 3.8775 | 81.52 | 7500 | 4.2286 | 31.1379 | 10.0975 | 22.5686 | 27.883 | 59.8 |
| 3.8455 | 82.61 | 7600 | 4.2292 | 32.076 | 10.0214 | 22.8866 | 28.3828 | 59.15 |
| 3.8838 | 83.7 | 7700 | 4.2269 | 31.5696 | 9.7812 | 22.7619 | 28.2236 | 58.6 |
| 3.8425 | 84.78 | 7800 | 4.2266 | 31.1731 | 9.97 | 22.4203 | 27.4956 | 59.1 |
| 3.8766 | 85.87 | 7900 | 4.2260 | 32.3221 | 10.6243 | 23.079 | 28.9008 | 58.45 |
| 3.8217 | 86.96 | 8000 | 4.2258 | 31.9956 | 10.4201 | 23.083 | 28.4945 | 58.5 |
| 3.8319 | 88.04 | 8100 | 4.2245 | 32.0272 | 10.4673 | 23.3471 | 28.9845 | 58.35 |
| 3.8283 | 89.13 | 8200 | 4.2231 | 32.2943 | 10.2594 | 23.1819 | 29.1345 | 60.5 |
| 3.8394 | 90.22 | 8300 | 4.2221 | 31.3976 | 10.3085 | 22.6581 | 28.2494 | 59.25 |
| 3.8258 | 91.3 | 8400 | 4.2203 | 31.4433 | 10.1184 | 22.672 | 28.1236 | 58.85 |
| 3.7981 | 92.39 | 8500 | 4.2205 | 31.1313 | 10.0056 | 22.677 | 27.7409 | 59.075 |
| 3.8349 | 93.48 | 8600 | 4.2215 | 31.5779 | 10.0303 | 22.6155 | 28.0566 | 59.2 |
| 3.8225 | 94.57 | 8700 | 4.2201 | 31.9646 | 10.0643 | 22.7808 | 28.67 | 58.925 |
| 3.8145 | 95.65 | 8800 | 4.2193 | 32.0347 | 10.5103 | 23.095 | 28.6056 | 57.225 |
| 3.7771 | 96.74 | 8900 | 4.2180 | 30.8138 | 9.602 | 22.2649 | 27.7948 | 57.875 |
| 3.823 | 97.83 | 9000 | 4.2168 | 31.3785 | 9.7046 | 22.3877 | 28.2578 | 58.675 |
| 3.7701 | 98.91 | 9100 | 4.2169 | 31.4511 | 9.9183 | 22.6645 | 28.1932 | 59.0 |
| 3.773 | 100.0 | 9200 | 4.2169 | 31.7392 | 9.9669 | 22.5894 | 28.218 | 58.15 |
| 3.7661 | 101.09 | 9300 | 4.2161 | 31.5507 | 9.8992 | 22.4602 | 28.3357 | 58.375 |
| 3.7875 | 102.17 | 9400 | 4.2163 | 31.5145 | 9.5173 | 22.321 | 27.8613 | 58.375 |
| 3.7659 | 103.26 | 9500 | 4.2152 | 31.2967 | 9.8797 | 22.6247 | 28.0317 | 57.925 |
| 3.7576 | 104.35 | 9600 | 4.2139 | 31.5739 | 9.8376 | 22.7561 | 28.2318 | 58.4 |
| 3.7784 | 105.43 | 9700 | 4.2144 | 32.2269 | 10.2299 | 22.6582 | 28.6249 | 58.425 |
| 3.7356 | 106.52 | 9800 | 4.2139 | 32.3031 | 10.1505 | 22.7079 | 28.9052 | 58.475 |
| 3.7799 | 107.61 | 9900 | 4.2124 | 31.1334 | 9.1481 | 22.1297 | 27.5951 | 58.6 |
| 3.7269 | 108.7 | 10000 | 4.2122 | 31.6957 | 9.2874 | 22.4867 | 28.225 | 58.4 |
| 3.719 | 109.78 | 10100 | 4.2108 | 31.477 | 10.0245 | 22.4703 | 28.1316 | 58.075 |
| 3.7411 | 110.87 | 10200 | 4.2112 | 31.4165 | 9.9791 | 22.4396 | 28.3068 | 58.275 |
| 3.7135 | 111.96 | 10300 | 4.2122 | 31.4924 | 9.9864 | 22.496 | 28.2414 | 57.8 |
| 3.7317 | 113.04 | 10400 | 4.2120 | 31.6599 | 10.1605 | 22.5322 | 28.3045 | 59.075 |
| 3.7113 | 114.13 | 10500 | 4.2127 | 31.6814 | 10.106 | 22.4311 | 28.5808 | 59.5 |
| 3.7063 | 115.22 | 10600 | 4.2132 | 31.2448 | 10.0006 | 22.5549 | 28.4686 | 57.775 |
| 3.681 | 116.3 | 10700 | 4.2123 | 31.1739 | 10.0533 | 22.2954 | 28.0822 | 58.35 |
| 3.7369 | 117.39 | 10800 | 4.2118 | 31.8541 | 10.1452 | 22.7607 | 28.9501 | 58.8 |
| 3.6645 | 118.48 | 10900 | 4.2122 | 31.7128 | 9.8554 | 22.4464 | 28.5888 | 58.375 |
| 3.6766 | 119.57 | 11000 | 4.2118 | 31.1492 | 9.8058 | 22.0978 | 28.1827 | 58.725 |
| 3.6915 | 120.65 | 11100 | 4.2110 | 31.1679 | 9.5755 | 22.1391 | 28.0886 | 58.375 |
| 3.6702 | 121.74 | 11200 | 4.2129 | 31.0682 | 9.7375 | 22.0118 | 28.2189 | 59.15 |
| 3.6946 | 122.83 | 11300 | 4.2118 | 31.6134 | 9.5918 | 22.2506 | 28.5343 | 59.175 |
| 3.6713 | 123.91 | 11400 | 4.2110 | 31.3585 | 9.4211 | 22.1884 | 27.8744 | 59.05 |
| 3.6694 | 125.0 | 11500 | 4.2126 | 32.0058 | 9.6453 | 22.3911 | 28.6928 | 59.55 |
| 3.6585 | 126.09 | 11600 | 4.2123 | 31.7679 | 9.7101 | 22.2378 | 28.4985 | 59.2 |
| 3.6857 | 127.17 | 11700 | 4.2118 | 31.7766 | 10.0375 | 22.5097 | 28.8104 | 59.6 |
| 3.6338 | 128.26 | 11800 | 4.2126 | 32.2508 | 10.2617 | 22.6745 | 29.0714 | 59.075 |
| 3.6412 | 129.35 | 11900 | 4.2135 | 32.0515 | 10.0905 | 22.7015 | 29.0028 | 58.9 |
| 3.6594 | 130.43 | 12000 | 4.2122 | 32.7784 | 10.351 | 23.0969 | 29.6672 | 59.525 |
| 3.6571 | 131.52 | 12100 | 4.2120 | 32.3165 | 10.329 | 22.8445 | 29.2886 | 59.5 |
| 3.6002 | 132.61 | 12200 | 4.2120 | 32.5553 | 10.0875 | 22.6064 | 29.1046 | 59.425 |
| 3.6621 | 133.7 | 12300 | 4.2126 | 31.7637 | 9.9785 | 22.5716 | 28.7173 | 59.275 |
| 3.6651 | 134.78 | 12400 | 4.2122 | 31.7568 | 9.7503 | 22.3876 | 28.6015 | 59.6 |
| 3.6127 | 135.87 | 12500 | 4.2123 | 31.5708 | 9.5203 | 21.9951 | 28.2082 | 58.75 |
| 3.6544 | 136.96 | 12600 | 4.2124 | 32.0767 | 9.8955 | 22.2724 | 28.4755 | 59.5 |
| 3.5994 | 138.04 | 12700 | 4.2125 | 31.8523 | 9.9159 | 22.2978 | 28.8159 | 59.175 |
| 3.6174 | 139.13 | 12800 | 4.2114 | 32.2165 | 9.784 | 22.4377 | 28.5603 | 59.1 |
| 3.6122 | 140.22 | 12900 | 4.2115 | 32.0247 | 9.6881 | 22.3116 | 28.61 | 58.9 |
| 3.6174 | 141.3 | 13000 | 4.2116 | 31.9549 | 9.5924 | 22.3997 | 28.9145 | 59.15 |
| 3.5965 | 142.39 | 13100 | 4.2113 | 32.6173 | 10.4241 | 22.8644 | 29.3928 | 60.9 |
| 3.6076 | 143.48 | 13200 | 4.2112 | 33.0058 | 10.6417 | 23.0297 | 29.8375 | 61.0 |
| 3.6013 | 144.57 | 13300 | 4.2105 | 33.005 | 10.5398 | 22.9758 | 29.7266 | 60.325 |
| 3.6181 | 145.65 | 13400 | 4.2117 | 31.0558 | 9.4714 | 21.9025 | 27.9627 | 60.025 |
| 3.6288 | 146.74 | 13500 | 4.2107 | 32.7196 | 10.4991 | 22.9182 | 29.6586 | 60.25 |
| 3.5879 | 147.83 | 13600 | 4.2091 | 32.6755 | 10.3936 | 22.9559 | 29.5314 | 60.425 |
| 3.591 | 148.91 | 13700 | 4.2101 | 33.2956 | 10.6616 | 22.8509 | 29.5237 | 60.4 |
| 3.5658 | 150.0 | 13800 | 4.2116 | 33.4712 | 10.3725 | 23.1449 | 30.0987 | 60.2 |
| 3.574 | 151.09 | 13900 | 4.2115 | 33.5427 | 10.5852 | 22.9671 | 29.8456 | 60.175 |
| 3.5795 | 152.17 | 14000 | 4.2115 | 33.4387 | 10.5744 | 23.4785 | 30.0494 | 60.15 |
| 3.5728 | 153.26 | 14100 | 4.2119 | 33.1244 | 10.0308 | 22.8377 | 29.7725 | 60.775 |
| 3.5441 | 154.35 | 14200 | 4.2121 | 32.9226 | 9.9625 | 22.9013 | 29.6004 | 59.7 |
| 3.5236 | 155.43 | 14300 | 4.2114 | 32.3717 | 9.9122 | 22.78 | 28.8305 | 59.725 |
| 3.5679 | 156.52 | 14400 | 4.2120 | 33.6347 | 10.7457 | 23.5191 | 30.1966 | 60.65 |
| 3.5574 | 157.61 | 14500 | 4.2119 | 33.4821 | 10.986 | 23.3567 | 30.1972 | 60.1 |
| 3.5935 | 158.7 | 14600 | 4.2115 | 32.7255 | 10.2639 | 23.1617 | 29.8065 | 60.35 |
| 3.5316 | 159.78 | 14700 | 4.2118 | 32.8033 | 10.0216 | 22.7099 | 29.3968 | 60.525 |
| 3.5618 | 160.87 | 14800 | 4.2118 | 32.6244 | 10.7228 | 22.8601 | 29.3613 | 60.8 |
| 3.545 | 161.96 | 14900 | 4.2132 | 32.6231 | 10.0711 | 22.4686 | 29.5341 | 59.675 |
| 3.5466 | 163.04 | 15000 | 4.2129 | 32.7601 | 10.3376 | 22.2373 | 29.3588 | 59.4 |
| 3.5594 | 164.13 | 15100 | 4.2127 | 32.4645 | 10.5106 | 22.6804 | 29.6229 | 60.375 |
| 3.4839 | 165.22 | 15200 | 4.2130 | 32.1799 | 10.0462 | 22.5474 | 29.1419 | 59.75 |
| 3.5492 | 166.3 | 15300 | 4.2133 | 32.6831 | 10.5307 | 22.8539 | 29.6406 | 59.875 |
| 3.5053 | 167.39 | 15400 | 4.2133 | 32.8614 | 10.0344 | 23.0577 | 29.5848 | 60.975 |
| 3.5427 | 168.48 | 15500 | 4.2140 | 32.7897 | 10.178 | 22.6287 | 29.4839 | 60.1 |
| 3.5495 | 169.57 | 15600 | 4.2126 | 33.1428 | 10.2866 | 22.9377 | 29.6883 | 60.525 |
| 3.5245 | 170.65 | 15700 | 4.2116 | 32.9892 | 10.1082 | 23.1528 | 29.576 | 60.675 |
| 3.5121 | 171.74 | 15800 | 4.2131 | 33.2677 | 10.5916 | 23.3002 | 29.8222 | 59.975 |
| 3.5559 | 172.83 | 15900 | 4.2126 | 32.5155 | 9.9557 | 22.6846 | 29.1171 | 60.85 |
| 3.4758 | 173.91 | 16000 | 4.2133 | 32.374 | 9.9127 | 22.4816 | 29.2839 | 60.9 |
| 3.5148 | 175.0 | 16100 | 4.2125 | 32.5611 | 9.8266 | 22.5993 | 28.9821 | 61.1 |
| 3.5093 | 176.09 | 16200 | 4.2132 | 32.1092 | 9.6761 | 22.3612 | 28.7771 | 60.05 |
| 3.5248 | 177.17 | 16300 | 4.2143 | 32.2696 | 9.6471 | 22.2791 | 28.9759 | 60.925 |
| 3.4807 | 178.26 | 16400 | 4.2139 | 31.9593 | 9.3878 | 22.0643 | 28.5392 | 61.3 |
| 3.5138 | 179.35 | 16500 | 4.2144 | 32.0284 | 9.8303 | 22.5724 | 29.0168 | 59.95 |
| 3.4834 | 180.43 | 16600 | 4.2153 | 32.3203 | 9.5741 | 22.4998 | 28.8014 | 60.5 |
| 3.4701 | 181.52 | 16700 | 4.2156 | 31.7243 | 9.544 | 22.1355 | 28.2238 | 61.275 |
| 3.5501 | 182.61 | 16800 | 4.2152 | 32.519 | 9.9372 | 22.3881 | 28.8347 | 61.45 |
| 3.4789 | 183.7 | 16900 | 4.2148 | 32.3324 | 9.7556 | 22.2474 | 28.7559 | 61.575 |
| 3.5172 | 184.78 | 17000 | 4.2156 | 32.161 | 9.4847 | 22.2358 | 28.8895 | 60.95 |
| 3.4681 | 185.87 | 17100 | 4.2167 | 32.6524 | 9.7116 | 22.8415 | 29.0798 | 60.575 |
| 3.4936 | 186.96 | 17200 | 4.2173 | 32.533 | 9.9478 | 22.7379 | 29.1301 | 61.575 |
| 3.4664 | 188.04 | 17300 | 4.2165 | 32.4549 | 10.1094 | 22.7097 | 28.7992 | 61.4 |
| 3.4599 | 189.13 | 17400 | 4.2164 | 32.6665 | 10.3463 | 22.7678 | 29.308 | 61.575 |
| 3.4724 | 190.22 | 17500 | 4.2175 | 32.4146 | 10.1782 | 22.7414 | 29.3546 | 60.75 |
| 3.4923 | 191.3 | 17600 | 4.2163 | 32.3624 | 9.8306 | 22.7311 | 28.7497 | 59.825 |
| 3.4771 | 192.39 | 17700 | 4.2161 | 33.1427 | 10.429 | 23.462 | 29.6967 | 60.35 |
| 3.4737 | 193.48 | 17800 | 4.2168 | 31.6894 | 9.7073 | 22.527 | 28.3711 | 60.65 |
| 3.4307 | 194.57 | 17900 | 4.2182 | 32.4769 | 10.1673 | 22.8356 | 29.4565 | 60.75 |
| 3.4843 | 195.65 | 18000 | 4.2168 | 32.5461 | 10.2855 | 22.8587 | 29.1242 | 60.825 |
| 3.4479 | 196.74 | 18100 | 4.2170 | 32.9284 | 10.2293 | 23.2679 | 29.8067 | 61.075 |
| 3.489 | 197.83 | 18200 | 4.2180 | 32.9561 | 10.481 | 23.2807 | 29.5499 | 61.25 |
| 3.4596 | 198.91 | 18300 | 4.2179 | 33.1418 | 10.2768 | 22.8762 | 30.0241 | 61.2 |
| 3.4552 | 200.0 | 18400 | 4.2171 | 33.5524 | 10.5969 | 23.5734 | 30.1587 | 61.525 |
| 3.4699 | 201.09 | 18500 | 4.2176 | 33.1941 | 10.3296 | 23.1962 | 30.1624 | 61.45 |
| 3.4281 | 202.17 | 18600 | 4.2187 | 33.3715 | 10.1919 | 23.1843 | 30.3192 | 61.55 |
| 3.4561 | 203.26 | 18700 | 4.2186 | 32.5288 | 9.9299 | 22.6515 | 29.2853 | 61.575 |
| 3.446 | 204.35 | 18800 | 4.2188 | 33.4268 | 10.7152 | 23.6525 | 30.4668 | 61.575 |
| 3.4259 | 205.43 | 18900 | 4.2189 | 33.1715 | 10.198 | 22.9264 | 29.8387 | 61.25 |
| 3.4497 | 206.52 | 19000 | 4.2192 | 33.3472 | 10.5372 | 23.0833 | 30.2925 | 61.25 |
| 3.4674 | 207.61 | 19100 | 4.2192 | 32.7581 | 10.2502 | 23.0554 | 29.6639 | 61.175 |
| 3.4521 | 208.7 | 19200 | 4.2186 | 33.7883 | 10.8639 | 23.4038 | 30.6114 | 61.475 |
| 3.443 | 209.78 | 19300 | 4.2194 | 33.029 | 10.6622 | 22.9009 | 29.9762 | 61.675 |
| 3.4356 | 210.87 | 19400 | 4.2199 | 32.7229 | 9.9204 | 22.5445 | 29.5517 | 61.3 |
| 3.4198 | 211.96 | 19500 | 4.2208 | 33.5216 | 10.3836 | 22.9423 | 29.9006 | 61.625 |
| 3.4417 | 213.04 | 19600 | 4.2210 | 32.7772 | 10.3206 | 22.9031 | 29.3774 | 61.625 |
| 3.4348 | 214.13 | 19700 | 4.2214 | 31.9959 | 10.0821 | 22.2012 | 28.6722 | 61.375 |
| 3.4528 | 215.22 | 19800 | 4.2213 | 32.5434 | 10.2807 | 22.6512 | 29.1705 | 61.65 |
| 3.3955 | 216.3 | 19900 | 4.2220 | 32.9148 | 10.5869 | 22.8107 | 29.4975 | 61.675 |
| 3.4437 | 217.39 | 20000 | 4.2227 | 32.8879 | 10.4334 | 22.6863 | 29.6794 | 61.125 |
| 3.4374 | 218.48 | 20100 | 4.2225 | 32.1453 | 9.9115 | 22.2936 | 28.9428 | 61.1 |
| 3.429 | 219.57 | 20200 | 4.2230 | 33.0805 | 10.5792 | 22.9417 | 29.9572 | 61.55 |
| 3.4089 | 220.65 | 20300 | 4.2239 | 32.0499 | 10.1613 | 22.6264 | 28.9217 | 61.65 |
| 3.418 | 221.74 | 20400 | 4.2237 | 32.6069 | 10.5032 | 22.8024 | 29.5804 | 61.275 |
| 3.4274 | 222.83 | 20500 | 4.2235 | 31.8624 | 10.2513 | 22.2816 | 28.8234 | 61.2 |
| 3.4156 | 223.91 | 20600 | 4.2242 | 32.2666 | 10.4604 | 22.5607 | 29.0666 | 61.025 |
| 3.4135 | 225.0 | 20700 | 4.2247 | 31.3445 | 10.0898 | 22.0664 | 28.5988 | 60.5 |
| 3.4283 | 226.09 | 20800 | 4.2245 | 31.47 | 10.0171 | 21.9423 | 28.4329 | 61.175 |
| 3.4048 | 227.17 | 20900 | 4.2242 | 31.93 | 10.4874 | 22.5287 | 29.1292 | 60.7 |
| 3.3925 | 228.26 | 21000 | 4.2243 | 32.3618 | 10.0902 | 22.6176 | 29.2689 | 60.775 |
| 3.4371 | 229.35 | 21100 | 4.2245 | 32.174 | 10.0424 | 22.516 | 28.9855 | 60.775 |
| 3.3789 | 230.43 | 21200 | 4.2239 | 33.0237 | 10.8644 | 23.3016 | 29.916 | 61.275 |
| 3.4109 | 231.52 | 21300 | 4.2248 | 32.88 | 10.6969 | 22.8426 | 30.0468 | 60.8 |
| 3.4128 | 232.61 | 21400 | 4.2257 | 32.6551 | 10.6032 | 22.6787 | 29.5307 | 60.725 |
| 3.3941 | 233.7 | 21500 | 4.2266 | 31.9296 | 10.0718 | 22.5 | 28.9451 | 60.75 |
| 3.3734 | 234.78 | 21600 | 4.2266 | 32.4862 | 10.0754 | 22.9705 | 29.2087 | 61.225 |
| 3.4144 | 235.87 | 21700 | 4.2269 | 32.1757 | 10.1225 | 22.6842 | 29.1731 | 60.75 |
| 3.3986 | 236.96 | 21800 | 4.2273 | 32.3403 | 10.481 | 22.7186 | 29.3236 | 60.725 |
| 3.3898 | 238.04 | 21900 | 4.2275 | 32.4957 | 10.4595 | 22.8682 | 29.6414 | 60.8 |
| 3.4031 | 239.13 | 22000 | 4.2275 | 32.4625 | 10.3807 | 22.7121 | 29.5187 | 60.725 |
| 3.3836 | 240.22 | 22100 | 4.2274 | 31.8107 | 10.2075 | 22.4437 | 28.9719 | 60.725 |
| 3.4084 | 241.3 | 22200 | 4.2272 | 32.3374 | 10.1027 | 22.5784 | 29.2192 | 61.2 |
| 3.3805 | 242.39 | 22300 | 4.2276 | 32.2783 | 10.375 | 22.7825 | 29.3762 | 61.2 |
| 3.3815 | 243.48 | 22400 | 4.2277 | 32.3337 | 10.3561 | 22.8489 | 29.4485 | 61.15 |
| 3.418 | 244.57 | 22500 | 4.2273 | 32.333 | 10.2841 | 22.8481 | 29.403 | 61.125 |
| 3.369 | 245.65 | 22600 | 4.2277 | 32.038 | 10.3555 | 22.6939 | 29.242 | 60.7 |
| 3.4305 | 246.74 | 22700 | 4.2276 | 32.7594 | 10.6867 | 23.0632 | 29.5852 | 61.575 |
| 3.3928 | 247.83 | 22800 | 4.2282 | 32.4979 | 10.5013 | 22.7875 | 29.4793 | 61.55 |
| 3.3676 | 248.91 | 22900 | 4.2286 | 32.6014 | 10.5697 | 22.8526 | 29.7876 | 61.6 |
| 3.3918 | 250.0 | 23000 | 4.2288 | 32.4746 | 10.6321 | 22.586 | 29.6323 | 60.675 |
| 3.395 | 251.09 | 23100 | 4.2294 | 32.4704 | 10.5456 | 22.6785 | 29.5769 | 60.725 |
| 3.363 | 252.17 | 23200 | 4.2296 | 32.2721 | 10.2554 | 22.5303 | 29.4554 | 60.725 |
| 3.3884 | 253.26 | 23300 | 4.2298 | 32.2746 | 10.434 | 22.6686 | 29.4486 | 60.725 |
| 3.3891 | 254.35 | 23400 | 4.2296 | 32.5382 | 10.5112 | 23.0243 | 29.8106 | 61.125 |
| 3.3679 | 255.43 | 23500 | 4.2296 | 32.4656 | 10.5631 | 22.9952 | 29.6832 | 61.125 |
| 3.4078 | 256.52 | 23600 | 4.2297 | 32.3377 | 10.4791 | 22.8362 | 29.6212 | 60.7 |
| 3.3642 | 257.61 | 23700 | 4.2302 | 32.2519 | 10.5551 | 22.6957 | 29.3763 | 61.075 |
| 3.3745 | 258.7 | 23800 | 4.2300 | 31.9413 | 10.4752 | 22.7447 | 29.1 | 61.175 |
| 3.3844 | 259.78 | 23900 | 4.2305 | 32.237 | 10.5492 | 23.0342 | 29.4079 | 61.65 |
| 3.3501 | 260.87 | 24000 | 4.2302 | 31.9797 | 10.4631 | 22.9089 | 29.332 | 61.65 |
| 3.4259 | 261.96 | 24100 | 4.2304 | 31.7515 | 10.3564 | 22.5923 | 29.1275 | 61.175 |
| 3.3578 | 263.04 | 24200 | 4.2309 | 32.0462 | 10.3883 | 22.9083 | 29.3591 | 61.65 |
| 3.39 | 264.13 | 24300 | 4.2308 | 31.9307 | 10.3057 | 22.8501 | 29.2547 | 61.65 |
| 3.3805 | 265.22 | 24400 | 4.2312 | 32.1836 | 10.3577 | 23.1293 | 29.4325 | 61.65 |
| 3.3667 | 266.3 | 24500 | 4.2309 | 32.1545 | 10.301 | 23.0613 | 29.343 | 61.65 |
| 3.3977 | 267.39 | 24600 | 4.2313 | 31.9549 | 10.2824 | 23.0397 | 29.2684 | 61.65 |
| 3.3434 | 268.48 | 24700 | 4.2314 | 31.9432 | 10.167 | 23.098 | 29.2669 | 61.65 |
| 3.3577 | 269.57 | 24800 | 4.2316 | 31.9679 | 10.3075 | 23.0715 | 29.3077 | 61.65 |
| 3.3781 | 270.65 | 24900 | 4.2317 | 32.2292 | 10.2988 | 23.0879 | 29.4171 | 61.65 |
| 3.3514 | 271.74 | 25000 | 4.2321 | 32.1653 | 10.4198 | 23.0554 | 29.3574 | 61.65 |
| 3.3935 | 272.83 | 25100 | 4.2320 | 32.134 | 10.2884 | 22.9444 | 29.2272 | 61.65 |
| 3.3447 | 273.91 | 25200 | 4.2324 | 32.3498 | 10.4505 | 23.0734 | 29.4438 | 61.65 |
| 3.3872 | 275.0 | 25300 | 4.2323 | 32.1743 | 10.4152 | 22.9462 | 29.3187 | 61.65 |
| 3.3755 | 276.09 | 25400 | 4.2324 | 32.2311 | 10.372 | 22.9563 | 29.3285 | 61.65 |
| 3.3832 | 277.17 | 25500 | 4.2323 | 32.0289 | 10.2105 | 22.9636 | 29.1449 | 61.65 |
| 3.3367 | 278.26 | 25600 | 4.2321 | 32.3053 | 10.2512 | 23.0834 | 29.4111 | 61.65 |
| 3.3767 | 279.35 | 25700 | 4.2323 | 32.4099 | 10.2793 | 23.0137 | 29.4049 | 61.65 |
| 3.3989 | 280.43 | 25800 | 4.2324 | 32.3471 | 10.4356 | 23.0179 | 29.4453 | 61.65 |
| 3.3625 | 281.52 | 25900 | 4.2325 | 32.2213 | 10.4363 | 22.9573 | 29.2886 | 61.65 |
| 3.3352 | 282.61 | 26000 | 4.2328 | 32.713 | 10.7489 | 23.2367 | 29.8725 | 61.65 |
| 3.3899 | 283.7 | 26100 | 4.2328 | 32.2145 | 10.2347 | 22.7896 | 29.2107 | 61.65 |
| 3.359 | 284.78 | 26200 | 4.2327 | 32.2466 | 10.4236 | 22.916 | 29.4227 | 61.65 |
| 3.3866 | 285.87 | 26300 | 4.2327 | 32.2466 | 10.4236 | 22.916 | 29.4227 | 61.65 |
| 3.3845 | 286.96 | 26400 | 4.2328 | 32.2466 | 10.4236 | 22.916 | 29.4227 | 61.65 |
| 3.3486 | 288.04 | 26500 | 4.2328 | 32.595 | 10.5041 | 23.1214 | 29.69 | 61.65 |
| 3.3807 | 289.13 | 26600 | 4.2328 | 32.759 | 10.566 | 23.3108 | 29.8555 | 61.65 |
| 3.3676 | 290.22 | 26700 | 4.2330 | 32.759 | 10.566 | 23.3108 | 29.8555 | 61.65 |
| 3.3361 | 291.3 | 26800 | 4.2332 | 32.759 | 10.566 | 23.3108 | 29.8555 | 61.65 |
| 3.3897 | 292.39 | 26900 | 4.2331 | 32.7251 | 10.566 | 23.3108 | 29.7958 | 61.65 |
| 3.3579 | 293.48 | 27000 | 4.2331 | 32.759 | 10.566 | 23.3108 | 29.8555 | 61.65 |
| 3.3809 | 294.57 | 27100 | 4.2331 | 32.759 | 10.566 | 23.3108 | 29.8555 | 61.65 |
| 3.3885 | 295.65 | 27200 | 4.2331 | 32.759 | 10.566 | 23.3108 | 29.8555 | 61.65 |
| 3.3173 | 296.74 | 27300 | 4.2331 | 32.7156 | 10.5699 | 23.2759 | 29.7903 | 61.65 |
| 3.3648 | 297.83 | 27400 | 4.2331 | 32.7156 | 10.5699 | 23.2759 | 29.7903 | 61.65 |
| 3.3793 | 298.91 | 27500 | 4.2331 | 32.7156 | 10.5699 | 23.2759 | 29.7903 | 61.65 |
| 3.3604 | 300.0 | 27600 | 4.2331 | 32.7156 | 10.5699 | 23.2759 | 29.7903 | 61.65 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
pietrolesci/bert-base-uncased-mnli | df493f6a1838576b54552afcee3a08dabb7579b2 | 2022-05-03T10:10:29.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | pietrolesci | null | pietrolesci/bert-base-uncased-mnli | 17 | null | transformers | 9,069 | Entry not found |
arxyzan/data2vec-roberta-base | 68434a0eeab8ff055b5ca13aa7e9a972233948aa | 2022-05-17T06:05:15.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2202.03555",
"transformers"
] | feature-extraction | false | arxyzan | null | arxyzan/data2vec-roberta-base | 17 | null | transformers | 9,070 | A RoBERTa model trained using Data2Vec based on the paper [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555).<br>
This model is provided here for [this repo](https://github.com/AryanShekarlaban/data2vec-pytorch) but was NOT trained using that codebase but instead, copied from `facebook/data2vec-text-base` for convenience and reproducibility.
### BibTeX entry and citation info
```bibtex
@misc{https://doi.org/10.48550/arxiv.2202.03555,
doi = {10.48550/ARXIV.2202.03555},
url = {https://arxiv.org/abs/2202.03555},
author = {Baevski, Alexei and Hsu, Wei-Ning and Xu, Qiantong and Babu, Arun and Gu, Jiatao and Auli, Michael},
keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
``` |
TweebankNLP/bertweet-tb2-ner | 773f0129e5bb057190d69e79068f23391f0deb7b | 2022-05-05T00:23:29.000Z | [
"pytorch",
"roberta",
"token-classification",
"arxiv:2201.07281",
"transformers",
"license:cc-by-nc-4.0",
"autotrain_compatible"
] | token-classification | false | TweebankNLP | null | TweebankNLP/bertweet-tb2-ner | 17 | null | transformers | 9,071 | ---
license: cc-by-nc-4.0
---
## Model Specification
- This is one **baseline Twitter NER model (with 73.71\% Entity-Level F1)** on Tweebank V2's NER benchmark (also called `Tweebank-NER`), trained on the Tweebank-NER training data.
- **If you are looking for the SOTA Twitter NER model**, please go to this [HuggingFace hub link](https://huggingface.co/TweebankNLP/bertweet-tb2_wnut17-ner).
- For more details about the `TweebankNLP` project, please refer to this [our paper](https://arxiv.org/pdf/2201.07281.pdf) and [github](https://github.com/social-machines/TweebankNLP) page.
- In the paper, it is referred as `HuggingFace-BERTweet (TB2)` in the NER table.
## How to use the model
- **PRE-PROCESSING**: when you apply the model on tweets, please make sure that tweets are preprocessed by the [TweetTokenizer](https://github.com/VinAIResearch/BERTweet/blob/master/TweetNormalizer.py) to get the best performance.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("TweebankNLP/bertweet-tb2-ner")
model = AutoModelForTokenClassification.from_pretrained("TweebankNLP/bertweet-tb2-ner")
```
## References
If you use this repository in your research, please kindly cite [our paper](https://arxiv.org/pdf/2201.07281.pdf):
```bibtex
@article{jiang2022tweetnlp,
title={Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis},
author={Jiang, Hang and Hua, Yining and Beeferman, Doug and Roy, Deb},
journal={In Proceedings of the 13th Language Resources and Evaluation Conference (LREC)},
year={2022}
}
``` |
Wakaka/bert-finetuned-imdb | 000f4675fd6b9dab2afadd4b79f35cfa9d56698f | 2022-05-06T06:38:19.000Z | [
"pytorch",
"bert",
"text-classification",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | Wakaka | null | Wakaka/bert-finetuned-imdb | 17 | null | transformers | 9,072 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: bert-finetuned-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.866
---
<!-- 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-finetuned-imdb
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: 0.5591
- Accuracy: 0.866
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 125 | 0.4995 | 0.79 |
| No log | 2.0 | 250 | 0.4000 | 0.854 |
| No log | 3.0 | 375 | 0.5591 | 0.866 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
eslamxm/mt5-base-finetuned-persian-finetuned-persian-arabic | 6213dea489fa88fa70afd5f55e8dce9e24495cb3 | 2022-05-09T05:50:11.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"dataset:xlsum",
"transformers",
"summarization",
"arabic",
"ar",
"Abstractive Summarization",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | eslamxm | null | eslamxm/mt5-base-finetuned-persian-finetuned-persian-arabic | 17 | null | transformers | 9,073 | ---
license: apache-2.0
tags:
- summarization
- arabic
- ar
- mt5
- Abstractive Summarization
- generated_from_trainer
datasets:
- xlsum
model-index:
- name: mt5-base-finetuned-persian-finetuned-persian-arabic
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-finetuned-persian-finetuned-persian-arabic
This model is a fine-tuned version of [ahmeddbahaa/mt5-base-finetuned-persian](https://huggingface.co/ahmeddbahaa/mt5-base-finetuned-persian) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3234
- Rouge-1: 22.96
- Rouge-2: 10.27
- Rouge-l: 20.95
- Gen Len: 19.0
- Bertscore: 71.59
## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 4.2754 | 1.0 | 1172 | 3.5717 | 19.26 | 7.26 | 17.48 | 19.0 | 70.49 |
| 3.7388 | 2.0 | 2344 | 3.4291 | 19.71 | 7.88 | 17.94 | 19.0 | 70.64 |
| 3.541 | 3.0 | 3516 | 3.3653 | 21.18 | 8.84 | 19.35 | 19.0 | 71.05 |
| 3.4113 | 4.0 | 4688 | 3.3306 | 21.54 | 9.11 | 19.65 | 19.0 | 71.19 |
| 3.3256 | 5.0 | 5860 | 3.3234 | 21.69 | 9.22 | 19.81 | 19.0 | 71.31 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
CEBaB/lstm.CEBaB.sa.5-class.exclusive.seed_42 | 81da16c37a9842d084e09fb98ce0eed9dd6e7174 | 2022-05-10T23:55:29.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | CEBaB | null | CEBaB/lstm.CEBaB.sa.5-class.exclusive.seed_42 | 17 | null | transformers | 9,074 | Entry not found |
CEBaB/lstm.CEBaB.sa.2-class.exclusive.seed_66 | 5926cb9615a5167fa024aa89e16c63763449e14d | 2022-05-11T00:12:47.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | CEBaB | null | CEBaB/lstm.CEBaB.sa.2-class.exclusive.seed_66 | 17 | null | transformers | 9,075 | Entry not found |
CEBaB/lstm.CEBaB.sa.3-class.exclusive.seed_66 | 203d0253d4d65b3e5f2fc468b9a3625af2092f3d | 2022-05-11T00:29:46.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | CEBaB | null | CEBaB/lstm.CEBaB.sa.3-class.exclusive.seed_66 | 17 | null | transformers | 9,076 | Entry not found |
CEBaB/lstm.CEBaB.sa.5-class.exclusive.seed_66 | 1f5d7fbecaa02c186081dc39a5f02fc44b6e92c6 | 2022-05-11T00:47:25.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | CEBaB | null | CEBaB/lstm.CEBaB.sa.5-class.exclusive.seed_66 | 17 | null | transformers | 9,077 | Entry not found |
CEBaB/lstm.CEBaB.sa.2-class.exclusive.seed_77 | 1bc9ad59b02f79207afed09a393b17cb63817eb3 | 2022-05-11T01:04:38.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | CEBaB | null | CEBaB/lstm.CEBaB.sa.2-class.exclusive.seed_77 | 17 | null | transformers | 9,078 | Entry not found |
CEBaB/lstm.CEBaB.sa.2-class.exclusive.seed_88 | 04dd7f7b2caa39f8c07dabf9d240decec4d9521e | 2022-05-11T01:57:06.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | CEBaB | null | CEBaB/lstm.CEBaB.sa.2-class.exclusive.seed_88 | 17 | null | transformers | 9,079 | Entry not found |
CEBaB/lstm.CEBaB.sa.3-class.exclusive.seed_88 | 2c35cae91b5a0f6ea6c6f18e04b5397966a8c69f | 2022-05-11T02:14:28.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | CEBaB | null | CEBaB/lstm.CEBaB.sa.3-class.exclusive.seed_88 | 17 | null | transformers | 9,080 | Entry not found |
CEBaB/lstm.CEBaB.sa.5-class.exclusive.seed_88 | 771f8dc662e5bb81aa34310c453e61b39396b90a | 2022-05-11T02:31:28.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | CEBaB | null | CEBaB/lstm.CEBaB.sa.5-class.exclusive.seed_88 | 17 | null | transformers | 9,081 | Entry not found |
CEBaB/lstm.CEBaB.sa.2-class.exclusive.seed_99 | 26c4fa8bb1925a918a48c637e3f9c0e869da4651 | 2022-05-11T02:48:32.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | CEBaB | null | CEBaB/lstm.CEBaB.sa.2-class.exclusive.seed_99 | 17 | null | transformers | 9,082 | Entry not found |
CEBaB/lstm.CEBaB.sa.3-class.exclusive.seed_99 | 4bfb3006a776db8aa19b5846581aeabab64a65f9 | 2022-05-11T03:05:48.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | CEBaB | null | CEBaB/lstm.CEBaB.sa.3-class.exclusive.seed_99 | 17 | null | transformers | 9,083 | Entry not found |
CEBaB/lstm.CEBaB.sa.5-class.exclusive.seed_99 | 057ec2347ae97c6eb4562e75b70da01a0250b1e8 | 2022-05-11T03:22:57.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | CEBaB | null | CEBaB/lstm.CEBaB.sa.5-class.exclusive.seed_99 | 17 | null | transformers | 9,084 | Entry not found |
SalamaThanks/SalamaThanksTransformer_fil2en_v2 | ed75269aa77cac1ada651a21f8c2777235a65090 | 2022-05-11T05:57:37.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | SalamaThanks | null | SalamaThanks/SalamaThanksTransformer_fil2en_v2 | 17 | null | transformers | 9,085 | ---
license: afl-3.0
---
SalamaThanks Transformer for Filipino-to-English Text Translation version 2.
A finetuned model based on the Helsinki-NLP/opus-mt-en-tl transformer model. |
Paleontolog/bert_sentence_classifier | 7a617b1f1dffb0f487af6a89fa92f2fed7ad7369 | 2022-05-11T14:05:26.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Paleontolog | null | Paleontolog/bert_sentence_classifier | 17 | null | transformers | 9,086 | Entry not found |
enoriega/kw_pubmed_5000_0.00006 | 7589c51c64d9b77b1dadf3b8d821190f4fcf92a9 | 2022-05-12T11:09:45.000Z | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | enoriega | null | enoriega/kw_pubmed_5000_0.00006 | 17 | null | transformers | 9,087 | Entry not found |
nikitast/lang-classifier-roberta | 33ed588b1fb6089c6e43c57917e067f4e3cebc11 | 2022-07-18T11:19:10.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"ru",
"uk",
"be",
"kk",
"az",
"hy",
"ka",
"he",
"en",
"de",
"dataset:open_subtitles",
"dataset:tatoeba",
"dataset:oscar",
"transformers",
"language classification"
] | text-classification | false | nikitast | null | nikitast/lang-classifier-roberta | 17 | 1 | transformers | 9,088 | ---
language:
- ru
- uk
- be
- kk
- az
- hy
- ka
- he
- en
- de
tags:
- language classification
datasets:
- open_subtitles
- tatoeba
- oscar
---
# RoBERTa for Single Language Classification
## Training
RoBERTa fine-tuned on small parts of Open Subtitles, Oscar and Tatoeba datasets (~9k samples per language).
| data source | language |
|-----------------|----------------|
| open_subtitles | ka, he, en, de |
| oscar | be, kk, az, hu |
| tatoeba | ru, uk |
## Validation
The metrics obtained from validation on the another part of dataset (~1k samples per language).
|index|class|f1-score|precision|recall|support|
|---|---|---|---|---|---|
|0|az|0\.998|0\.997|1\.0|997|
|1|be|0\.996|0\.998|0\.994|1004|
|2|de|0\.976|0\.966|0\.987|979|
|3|en|0\.976|0\.986|0\.967|1020|
|4|he|1\.0|1\.0|0\.999|1001|
|5|hy|0\.994|0\.991|0\.998|993|
|6|ka|0\.999|0\.999|0\.999|1000|
|7|kk|0\.996|0\.998|0\.993|1005|
|8|uk|0\.982|0\.997|0\.968|1030|
|9|ru|0\.982|0\.968|0\.997|971|
|10|macro\_avg|0\.99|0\.99|0\.99|10000|
|11|weighted avg|0\.99|0\.99|0\.99|10000| |
Bryan0123/bert-hashtag-to-hashtag-20 | eb089721e6a7585e6a5fe7a41474c9fd426157cf | 2022-05-15T05:02:12.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Bryan0123 | null | Bryan0123/bert-hashtag-to-hashtag-20 | 17 | null | transformers | 9,089 | Entry not found |
vives/distilbert-base-uncased-finetuned-cvent-2022 | de2d5128d93fe20949d25eb1ce7351ea78e0a489 | 2022-05-13T20:37:30.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | vives | null | vives/distilbert-base-uncased-finetuned-cvent-2022 | 17 | null | transformers | 9,090 | Entry not found |
dipstheman/DialoGPT-small-humanconversation | aa81c831d8303afbaf1522ce24f7f569185f3ce2 | 2022-05-16T22:05:07.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | dipstheman | null | dipstheman/DialoGPT-small-humanconversation | 17 | null | transformers | 9,091 | ---
tags:
- conversational
---
#human conversation DialoGPT Model |
SyedMujtabaHassanRizvi/convnext-tiny-finetuned-eurosat | cb9800974779afb36ab23ed01f92b41e77752d4e | 2022-05-19T12:48:40.000Z | [
"pytorch",
"convnext",
"image-classification",
"transformers"
] | image-classification | false | SyedMujtabaHassanRizvi | null | SyedMujtabaHassanRizvi/convnext-tiny-finetuned-eurosat | 17 | null | transformers | 9,092 | Entry not found |
animalthemuppet/bert-finetuned-ner | c5082885310360f718e076f7d05b9c19e5cf7e73 | 2022-05-22T17:04:06.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | animalthemuppet | null | animalthemuppet/bert-finetuned-ner | 17 | null | transformers | 9,093 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9306472919418758
- name: Recall
type: recall
value: 0.9485021878155503
- name: F1
type: f1
value: 0.9394899149858308
- name: Accuracy
type: accuracy
value: 0.9859304173779949
---
<!-- 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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0633
- Precision: 0.9306
- Recall: 0.9485
- F1: 0.9395
- Accuracy: 0.9859
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0834 | 1.0 | 1756 | 0.0676 | 0.9162 | 0.9315 | 0.9238 | 0.9824 |
| 0.0388 | 2.0 | 3512 | 0.0587 | 0.9286 | 0.9473 | 0.9379 | 0.9852 |
| 0.0188 | 3.0 | 5268 | 0.0633 | 0.9306 | 0.9485 | 0.9395 | 0.9859 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
eslamxm/mt5-base-finetuned-ar-sp | 0ff443165c15491cae6b60db5ca9cca22bdf693e | 2022-05-23T23:27:43.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"summarization",
"arabic",
"am",
"es",
"amharic",
"Abstractive Summarization",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | eslamxm | null | eslamxm/mt5-base-finetuned-ar-sp | 17 | null | transformers | 9,094 | ---
license: apache-2.0
tags:
- summarization
- arabic
- am
- es
- amharic
- mt5
- Abstractive Summarization
- generated_from_trainer
model-index:
- name: mt5-base-finetuned-ar-sp
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-finetuned-ar-sp
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2772
- Rouge-1: 23.01
- Rouge-2: 10.41
- Rouge-l: 20.94
- Gen Len: 19.0
- Bertscore: 71.56
## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 4.1968 | 1.0 | 1352 | 3.5142 | 18.69 | 6.73 | 16.97 | 19.0 | 70.3 |
| 3.6932 | 2.0 | 2704 | 3.3799 | 20.67 | 8.38 | 18.75 | 19.0 | 70.82 |
| 3.5058 | 3.0 | 4056 | 3.3184 | 20.97 | 8.58 | 19.08 | 19.0 | 71.08 |
| 3.3832 | 4.0 | 5408 | 3.2851 | 21.59 | 8.94 | 19.63 | 19.0 | 71.28 |
| 3.2994 | 5.0 | 6760 | 3.2772 | 21.84 | 9.23 | 19.85 | 19.0 | 71.34 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
hd94/roberta-hindi | 10c6f839598e6f2acc27ff67627d89ceb2e8dbda | 2022-05-24T09:42:28.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | hd94 | null | hd94/roberta-hindi | 17 | null | transformers | 9,095 | Entry not found |
Ravindra001/bert-finetuned-ner | 2967d6f51750d99db081eea1a9e5bf703c3bf439 | 2022-07-28T09:29:11.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | Ravindra001 | null | Ravindra001/bert-finetuned-ner | 17 | null | transformers | 9,096 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
args: en
metrics:
- name: Precision
type: precision
value: 0.819622641509434
- name: Recall
type: recall
value: 0.8444790046656299
- name: F1
type: f1
value: 0.8318651857525853
- name: Accuracy
type: accuracy
value: 0.9269227060339613
- task:
type: token-classification
name: Token Classification
dataset:
name: wikiann
type: wikiann
config: en
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.8492771401033908
verified: true
- name: Precision
type: precision
value: 0.857294905524994
verified: true
- name: Recall
type: recall
value: 0.865900059186607
verified: true
- name: F1
type: f1
value: 0.8615759964905745
verified: true
- name: loss
type: loss
value: 1.054654836654663
verified: true
---
<!-- 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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3217
- Precision: 0.8196
- Recall: 0.8445
- F1: 0.8319
- Accuracy: 0.9269
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2821 | 1.0 | 2500 | 0.2906 | 0.7983 | 0.8227 | 0.8103 | 0.9193 |
| 0.2087 | 2.0 | 5000 | 0.2614 | 0.8030 | 0.8379 | 0.8201 | 0.9257 |
| 0.1404 | 3.0 | 7500 | 0.3217 | 0.8196 | 0.8445 | 0.8319 | 0.9269 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Mathking/all-mpnet-base-v2_outcome_sim | af3847ab3ef6e74ac548712a0fe6a88a115b3485 | 2022-05-25T13:40:22.000Z | [
"pytorch",
"mpnet",
"feature-extraction",
"sentence-transformers",
"sentence-similarity"
] | sentence-similarity | false | Mathking | null | Mathking/all-mpnet-base-v2_outcome_sim | 17 | null | sentence-transformers | 9,097 | ---
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 48 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": 5,
"evaluation_steps": 100,
"evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 20,
"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 --> |
aditya2029/gpt-neo-genre-storygenerator | d63e6a511a2eab462b397d813f07ab6e79ec807c | 2022-05-26T02:27:55.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | aditya2029 | null | aditya2029/gpt-neo-genre-storygenerator | 17 | null | transformers | 9,098 | |
andidu/paraphrase-ru | 05678a1fae2802efc7ba76715569b3043a001b9a | 2022-05-28T07:05:58.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | andidu | null | andidu/paraphrase-ru | 17 | null | transformers | 9,099 | Entry not found |
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