modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
gsarti/it5-small-question-generation | gsarti | 2022-03-09T07:55:38Z | 14 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"italian",
"sequence-to-sequence",
"question-generation",
"squad_it",
"it",
"dataset:squad_it",
"arxiv:2203.03759",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language:
- it
license: apache-2.0
datasets:
- squad_it
tags:
- italian
- sequence-to-sequence
- question-generation
- squad_it
- text2text-generation
widget:
- text: "Le conoscenze mediche erano stagnanti durante il Medioevo. Il resoconto più autorevole di allora è venuto dalla facoltà di medicina di Parigi in un rapporto al re di Francia che ha incolpato i cieli, sotto forma di una congiunzione di tre pianeti nel 1345 che causò una \"grande pestilenza nell' aria\". Questa relazione è diventata la prima e più diffusa di una serie di casi di peste che cercava di dare consigli ai malati. Che la peste fosse causata dalla cattiva aria divenne la teoria più accettata. Oggi, questo è conosciuto come la teoria di Miasma. La parola \"peste\" non aveva un significato particolare in questo momento, e solo la ricorrenza dei focolai durante il Medioevo gli diede il nome che è diventato il termine medico. Risposta: re di Francia"
- text: "Il 14 aprile 2011, ABC ha annullato le lunghe opere di sapone All My Children e One Life to Live dopo 41 e 43 anni in onda, rispettivamente (in seguito al contraccolpo dei tifosi, ABC ha venduto i diritti ad entrambi gli spettacoli a Prospect Park, che alla fine ha rilanciato i saponi su Hulu per un' ulteriore stagione nel 2013 e con entrambe le società che si citano in giudizio per accuse di interferenza con il processo di rilancio degli spettacoli, mancato pagamento delle tasse di licenza. Il talk/lifestyle show che ha sostituito One Life to Live, The Revolution, non è riuscito a generare giudizi soddisfacenti ed è stato a sua volta annullato dopo soli sette mesi. La stagione 2011-12 ha visto l' ABC cadere al quarto posto nel 18-49 demografico nonostante rinnovando una manciata di nuovi spettacoli (compresi i drammi matricole Scandal, Revenge e Once Upon a Time) per la seconda stagione. Risposta: Hulu"
- text: "L' American Broadcasting Company (ABC) (stlized nel suo logo come abc dal 1957) è una rete televisiva commerciale americana trasmissione televisiva che è di proprietà del Disney-ABC Television Group, una controllata della divisione Disney Media Networks di The Walt Disney Company. La rete fa parte delle grandi reti televisive Big Three. La rete ha sede a Columbus Avenue e West 66th Street a Manhattan, con ulteriori uffici e stabilimenti di produzione a New York City, Los Angeles e Burbank, California. Risposta: Manhattan"
- text: "La disobbedienza civile non rivoluzionaria è una semplice disobbedienza delle leggi sulla base del fatto che sono giudicate \"sbagliate\" da una coscienza individuale, o come parte di uno sforzo per rendere alcune leggi inefficaci, per causarne l' abrogazione, o per esercitare pressioni per ottenere i propri desideri politici su qualche altra questione. La disobbedienza civile rivoluzionaria è più che altro un tentativo attivo di rovesciare un governo (o di cambiare le tradizioni culturali, i costumi sociali, le credenze religiose, ecc. La rivoluzione non deve necessariamente essere politica, cioè \"rivoluzione culturale\", implica semplicemente un cambiamento radicale e diffuso in una sezione del tessuto sociale). Gli atti di Gandhi sono stati descritti come disobbedienza civile rivoluzionaria. È stato affermato che gli ungheresi sotto Ferenc Deák hanno diretto una disobbedienza civile rivoluzionaria contro il governo austriaco. Thoreau ha anche scritto di disobbedienza civile realizzando \"rivoluzione pacifica\". Howard Zinn, Harvey Wheeler e altri hanno identificato il diritto sposato nella Dichiarazione d' Indipendenza di \"alterare o abolire\" un governo ingiusto come principio di disobbedienza civile. Risposta: Ferenc Deák"
metrics:
- rouge
- bertscore
model-index:
- name: it5-small-question-generation
results:
- task:
type: question-generation
name: "Question generation"
dataset:
type: squad_it
name: "SQuAD-IT"
metrics:
- type: rouge1
value: 0.367
name: "Test Rouge1"
- type: rouge2
value: 0.189
name: "Test Rouge2"
- type: rougeL
value: 0.344
name: "Test RougeL"
- type: bertscore
value: 0.505
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: "8g"
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 Small for Question Generation 💭 🇮🇹
This repository contains the checkpoint for the [IT5 Small](https://huggingface.co/gsarti/it5-small) model fine-tuned on question generation on the [SQuAD-IT corpus](https://huggingface.co/datasets/squad_it) 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
qg = pipeline("text2text-generation", model='it5/it5-small-question-generation')
qg("Le conoscenze mediche erano stagnanti durante il Medioevo. Il resoconto più autorevole di allora è venuto dalla facoltà di medicina di Parigi in un rapporto al re di Francia che ha incolpato i cieli, sotto forma di una congiunzione di tre pianeti nel 1345 che causò una "grande pestilenza nell\' aria". Questa relazione è diventata la prima e più diffusa di una serie di casi di peste che cercava di dare consigli ai malati. Che la peste fosse causata dalla cattiva aria divenne la teoria più accettata. Oggi, questo è conosciuto come la teoria di Miasma. La parola "peste" non aveva un significato particolare in questo momento, e solo la ricorrenza dei focolai durante il Medioevo gli diede il nome che è diventato il termine medico. Risposta: re di Francia")
>>> [{"generated_text": "Per chi è stato redatto il referto medico?"}]
```
or loaded using autoclasses:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("it5/it5-small-question-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("it5/it5-small-question-generation")
```
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}
}
``` |
gsarti/mt5-small-question-generation | gsarti | 2022-03-09T07:55:07Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"mt5",
"text2text-generation",
"italian",
"sequence-to-sequence",
"question-generation",
"squad_it",
"it",
"dataset:squad_it",
"arxiv:2203.03759",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language:
- it
license: apache-2.0
datasets:
- squad_it
tags:
- italian
- sequence-to-sequence
- question-generation
- squad_it
- text2text-generation
widget:
- text: "Le conoscenze mediche erano stagnanti durante il Medioevo. Il resoconto più autorevole di allora è venuto dalla facoltà di medicina di Parigi in un rapporto al re di Francia che ha incolpato i cieli, sotto forma di una congiunzione di tre pianeti nel 1345 che causò una \"grande pestilenza nell' aria\". Questa relazione è diventata la prima e più diffusa di una serie di casi di peste che cercava di dare consigli ai malati. Che la peste fosse causata dalla cattiva aria divenne la teoria più accettata. Oggi, questo è conosciuto come la teoria di Miasma. La parola \"peste\" non aveva un significato particolare in questo momento, e solo la ricorrenza dei focolai durante il Medioevo gli diede il nome che è diventato il termine medico. Risposta: re di Francia"
- text: "Il 14 aprile 2011, ABC ha annullato le lunghe opere di sapone All My Children e One Life to Live dopo 41 e 43 anni in onda, rispettivamente (in seguito al contraccolpo dei tifosi, ABC ha venduto i diritti ad entrambi gli spettacoli a Prospect Park, che alla fine ha rilanciato i saponi su Hulu per un' ulteriore stagione nel 2013 e con entrambe le società che si citano in giudizio per accuse di interferenza con il processo di rilancio degli spettacoli, mancato pagamento delle tasse di licenza. Il talk/lifestyle show che ha sostituito One Life to Live, The Revolution, non è riuscito a generare giudizi soddisfacenti ed è stato a sua volta annullato dopo soli sette mesi. La stagione 2011-12 ha visto l' ABC cadere al quarto posto nel 18-49 demografico nonostante rinnovando una manciata di nuovi spettacoli (compresi i drammi matricole Scandal, Revenge e Once Upon a Time) per la seconda stagione. Risposta: Hulu"
- text: "L' American Broadcasting Company (ABC) (stlized nel suo logo come abc dal 1957) è una rete televisiva commerciale americana trasmissione televisiva che è di proprietà del Disney-ABC Television Group, una controllata della divisione Disney Media Networks di The Walt Disney Company. La rete fa parte delle grandi reti televisive Big Three. La rete ha sede a Columbus Avenue e West 66th Street a Manhattan, con ulteriori uffici e stabilimenti di produzione a New York City, Los Angeles e Burbank, California. Risposta: Manhattan"
- text: "La disobbedienza civile non rivoluzionaria è una semplice disobbedienza delle leggi sulla base del fatto che sono giudicate \"sbagliate\" da una coscienza individuale, o come parte di uno sforzo per rendere alcune leggi inefficaci, per causarne l' abrogazione, o per esercitare pressioni per ottenere i propri desideri politici su qualche altra questione. La disobbedienza civile rivoluzionaria è più che altro un tentativo attivo di rovesciare un governo (o di cambiare le tradizioni culturali, i costumi sociali, le credenze religiose, ecc. La rivoluzione non deve necessariamente essere politica, cioè \"rivoluzione culturale\", implica semplicemente un cambiamento radicale e diffuso in una sezione del tessuto sociale). Gli atti di Gandhi sono stati descritti come disobbedienza civile rivoluzionaria. È stato affermato che gli ungheresi sotto Ferenc Deák hanno diretto una disobbedienza civile rivoluzionaria contro il governo austriaco. Thoreau ha anche scritto di disobbedienza civile realizzando \"rivoluzione pacifica\". Howard Zinn, Harvey Wheeler e altri hanno identificato il diritto sposato nella Dichiarazione d' Indipendenza di \"alterare o abolire\" un governo ingiusto come principio di disobbedienza civile. Risposta: Ferenc Deák"
metrics:
- rouge
- bertscore
model-index:
- name: mt5-small-question-generation
results:
- task:
type: question-generation
name: "Question generation"
dataset:
type: squad_it
name: "SQuAD-IT"
metrics:
- type: rouge1
value: 0.306
name: "Test Rouge1"
- type: rouge2
value: 0.143
name: "Test Rouge2"
- type: rougeL
value: 0.286
name: "Test RougeL"
- type: bertscore
value: 0.463
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
---
# mT5 Small for Question Generation 💭 🇮🇹
This repository contains the checkpoint for the [mT5 Small](https://huggingface.co/google/mt5-small) model fine-tuned on question generation on the [SQuAD-IT corpus](https://huggingface.co/datasets/squad_it) 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
qg = pipeline("text2text-generation", model='it5/mt5-small-question-generation')
qg("Le conoscenze mediche erano stagnanti durante il Medioevo. Il resoconto più autorevole di allora è venuto dalla facoltà di medicina di Parigi in un rapporto al re di Francia che ha incolpato i cieli, sotto forma di una congiunzione di tre pianeti nel 1345 che causò una "grande pestilenza nell\' aria". Questa relazione è diventata la prima e più diffusa di una serie di casi di peste che cercava di dare consigli ai malati. Che la peste fosse causata dalla cattiva aria divenne la teoria più accettata. Oggi, questo è conosciuto come la teoria di Miasma. La parola "peste" non aveva un significato particolare in questo momento, e solo la ricorrenza dei focolai durante il Medioevo gli diede il nome che è diventato il termine medico. Risposta: re di Francia")
>>> [{"generated_text": "Per chi è stato redatto il referto medico?"}]
```
or loaded using autoclasses:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("it5/mt5-small-question-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("it5/mt5-small-question-generation")
```
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}
}
``` |
gsarti/mt5-base-question-generation | gsarti | 2022-03-09T07:54:16Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"mt5",
"text2text-generation",
"italian",
"sequence-to-sequence",
"question-generation",
"squad_it",
"it",
"dataset:squad_it",
"arxiv:2203.03759",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language:
- it
license: apache-2.0
datasets:
- squad_it
tags:
- italian
- sequence-to-sequence
- question-generation
- squad_it
- text2text-generation
widget:
- text: "Le conoscenze mediche erano stagnanti durante il Medioevo. Il resoconto più autorevole di allora è venuto dalla facoltà di medicina di Parigi in un rapporto al re di Francia che ha incolpato i cieli, sotto forma di una congiunzione di tre pianeti nel 1345 che causò una \"grande pestilenza nell' aria\". Questa relazione è diventata la prima e più diffusa di una serie di casi di peste che cercava di dare consigli ai malati. Che la peste fosse causata dalla cattiva aria divenne la teoria più accettata. Oggi, questo è conosciuto come la teoria di Miasma. La parola \"peste\" non aveva un significato particolare in questo momento, e solo la ricorrenza dei focolai durante il Medioevo gli diede il nome che è diventato il termine medico. Risposta: re di Francia"
- text: "Il 14 aprile 2011, ABC ha annullato le lunghe opere di sapone All My Children e One Life to Live dopo 41 e 43 anni in onda, rispettivamente (in seguito al contraccolpo dei tifosi, ABC ha venduto i diritti ad entrambi gli spettacoli a Prospect Park, che alla fine ha rilanciato i saponi su Hulu per un' ulteriore stagione nel 2013 e con entrambe le società che si citano in giudizio per accuse di interferenza con il processo di rilancio degli spettacoli, mancato pagamento delle tasse di licenza. Il talk/lifestyle show che ha sostituito One Life to Live, The Revolution, non è riuscito a generare giudizi soddisfacenti ed è stato a sua volta annullato dopo soli sette mesi. La stagione 2011-12 ha visto l' ABC cadere al quarto posto nel 18-49 demografico nonostante rinnovando una manciata di nuovi spettacoli (compresi i drammi matricole Scandal, Revenge e Once Upon a Time) per la seconda stagione. Risposta: Hulu"
- text: "L' American Broadcasting Company (ABC) (stlized nel suo logo come abc dal 1957) è una rete televisiva commerciale americana trasmissione televisiva che è di proprietà del Disney-ABC Television Group, una controllata della divisione Disney Media Networks di The Walt Disney Company. La rete fa parte delle grandi reti televisive Big Three. La rete ha sede a Columbus Avenue e West 66th Street a Manhattan, con ulteriori uffici e stabilimenti di produzione a New York City, Los Angeles e Burbank, California. Risposta: Manhattan"
- text: "La disobbedienza civile non rivoluzionaria è una semplice disobbedienza delle leggi sulla base del fatto che sono giudicate \"sbagliate\" da una coscienza individuale, o come parte di uno sforzo per rendere alcune leggi inefficaci, per causarne l' abrogazione, o per esercitare pressioni per ottenere i propri desideri politici su qualche altra questione. La disobbedienza civile rivoluzionaria è più che altro un tentativo attivo di rovesciare un governo (o di cambiare le tradizioni culturali, i costumi sociali, le credenze religiose, ecc. La rivoluzione non deve necessariamente essere politica, cioè \"rivoluzione culturale\", implica semplicemente un cambiamento radicale e diffuso in una sezione del tessuto sociale). Gli atti di Gandhi sono stati descritti come disobbedienza civile rivoluzionaria. È stato affermato che gli ungheresi sotto Ferenc Deák hanno diretto una disobbedienza civile rivoluzionaria contro il governo austriaco. Thoreau ha anche scritto di disobbedienza civile realizzando \"rivoluzione pacifica\". Howard Zinn, Harvey Wheeler e altri hanno identificato il diritto sposato nella Dichiarazione d' Indipendenza di \"alterare o abolire\" un governo ingiusto come principio di disobbedienza civile. Risposta: Ferenc Deák"
metrics:
- rouge
- bertscore
model-index:
- name: mt5-base-question-generation
results:
- task:
type: question-generation
name: "Question generation"
dataset:
type: squad_it
name: "SQuAD-IT"
metrics:
- type: rouge1
value: 0.346
name: "Test Rouge1"
- type: rouge2
value: 0.174
name: "Test Rouge2"
- type: rougeL
value: 0.324
name: "Test RougeL"
- type: bertscore
value: 0.495
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: "40g"
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
---
# mT5 Base for Question Generation 💭 🇮🇹
This repository contains the checkpoint for the [mT5 Base](https://huggingface.co/google/mt5-base) model fine-tuned on question generation on the [SQuAD-IT corpus](https://huggingface.co/datasets/squad_it) 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
qg = pipeline("text2text-generation", model='it5/mt5-base-question-generation')
qg("Le conoscenze mediche erano stagnanti durante il Medioevo. Il resoconto più autorevole di allora è venuto dalla facoltà di medicina di Parigi in un rapporto al re di Francia che ha incolpato i cieli, sotto forma di una congiunzione di tre pianeti nel 1345 che causò una "grande pestilenza nell\' aria". Questa relazione è diventata la prima e più diffusa di una serie di casi di peste che cercava di dare consigli ai malati. Che la peste fosse causata dalla cattiva aria divenne la teoria più accettata. Oggi, questo è conosciuto come la teoria di Miasma. La parola "peste" non aveva un significato particolare in questo momento, e solo la ricorrenza dei focolai durante il Medioevo gli diede il nome che è diventato il termine medico. Risposta: re di Francia")
>>> [{"generated_text": "Per chi è stato redatto il referto medico?"}]
```
or loaded using autoclasses:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("it5/mt5-base-question-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("it5/mt5-base-question-generation")
```
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}
}
``` |
gsarti/mt5-base-wiki-summarization | gsarti | 2022-03-09T07:51:31Z | 13 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"mt5",
"text2text-generation",
"italian",
"sequence-to-sequence",
"wikipedia",
"summarization",
"wits",
"it",
"dataset:wits",
"arxiv:2203.03759",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | 2022-03-02T23:29:05Z | ---
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: mt5-base-wiki-summarization
results:
- task:
type: wiki-summarization
name: "Wikipedia Summarization"
dataset:
type: wits
name: "WITS"
metrics:
- type: rouge1
value: 0.348
name: "Test Rouge1"
- type: rouge2
value: 0.200
name: "Test Rouge2"
- type: rougeL
value: 0.315
name: "Test RougeL"
- type: bertscore
value: 0.520
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: "40g"
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
---
# mT5 Base for Wikipedia Summarization ✂️📑 🇮🇹
This repository contains the checkpoint for the [mT5 Base](https://huggingface.co/google/mt5-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
wikisum = pipeline("summarization", model='it5/mt5-base-wiki-summarization')
wikisum("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. ")
>>> [{"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/mt5-base-wiki-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("it5/mt5-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}
}
``` |
gsarti/mt5-base-informal-to-formal | gsarti | 2022-03-09T07:48:51Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"mt5",
"text2text-generation",
"italian",
"sequence-to-sequence",
"style-transfer",
"formality-style-transfer",
"it",
"dataset:yahoo/xformal_it",
"arxiv:2203.03759",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language:
- it
license: apache-2.0
tags:
- italian
- sequence-to-sequence
- style-transfer
- formality-style-transfer
datasets:
- yahoo/xformal_it
widget:
- text: "maronn qualcuno mi spieg' CHECCOSA SUCCEDE?!?!"
- text: "wellaaaaaaa, ma fraté sei proprio troppo simpatiko, grazieeee!!"
- text: "nn capisco xke tt i ragazzi lo fanno"
- text: "IT5 è SUPERMEGA BRAVISSIMO a capire tt il vernacolo italiano!!!"
metrics:
- rouge
- bertscore
model-index:
- name: mt5-base-informal-to-formal
results:
- task:
type: formality-style-transfer
name: "Informal-to-formal Style Transfer"
dataset:
type: xformal_it
name: "XFORMAL (Italian Subset)"
metrics:
- type: rouge1
value: 0.661
name: "Avg. Test Rouge1"
- type: rouge2
value: 0.471
name: "Avg. Test Rouge2"
- type: rougeL
value: 0.642
name: "Avg. Test RougeL"
- type: bertscore
value: 0.712
name: "Avg. 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: "40g"
source: "Google Cloud Platform Carbon Footprint"
training_type: "fine-tuning"
geographical_location: "Eemshaven, Netherlands, Europe"
hardware_used: "1 TPU v3-8 VM"
---
# mT5 Base for Informal-to-formal Style Transfer 🧐
This repository contains the checkpoint for the [mT5 Base](https://huggingface.co/google/mt5-base) model fine-tuned on Informal-to-formal style transfer on the Italian subset of the XFORMAL 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
i2f = pipeline("text2text-generation", model='it5/mt5-base-informal-to-formal')
i2f("nn capisco xke tt i ragazzi lo fanno")
>>> [{"generated_text": "non comprendo perché tutti i ragazzi agiscono così"}]
```
or loaded using autoclasses:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("it5/mt5-base-informal-to-formal")
model = AutoModelForSeq2SeqLM.from_pretrained("it5/mt5-base-informal-to-formal")
```
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}
}
``` |
gsarti/it5-large-formal-to-informal | gsarti | 2022-03-09T07:46:17Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"italian",
"sequence-to-sequence",
"style-transfer",
"formality-style-transfer",
"it",
"dataset:yahoo/xformal_it",
"arxiv:2203.03759",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language:
- it
license: apache-2.0
tags:
- italian
- sequence-to-sequence
- style-transfer
- formality-style-transfer
datasets:
- yahoo/xformal_it
widget:
- text: "Questa performance è a dir poco spiacevole."
- text: "In attesa di un Suo cortese riscontro, Le auguriamo un piacevole proseguimento di giornata."
- text: "Questa visione mi procura una goduria indescrivibile."
- text: "qualora ciò possa interessarti, ti pregherei di contattarmi."
metrics:
- rouge
- bertscore
model-index:
- name: it5-large-formal-to-informal
results:
- task:
type: formality-style-transfer
name: "Formal-to-informal Style Transfer"
dataset:
type: xformal_it
name: "XFORMAL (Italian Subset)"
metrics:
- type: rouge1
value: 0.611
name: "Avg. Test Rouge1"
- type: rouge2
value: 0.409
name: "Avg. Test Rouge2"
- type: rougeL
value: 0.586
name: "Avg. Test RougeL"
- type: bertscore
value: 0.613
name: "Avg. 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: "51g"
source: "Google Cloud Platform Carbon Footprint"
training_type: "fine-tuning"
geographical_location: "Eemshaven, Netherlands, Europe"
hardware_used: "1 TPU v3-8 VM"
---
# IT5 Large for Formal-to-informal Style Transfer 🤗
This repository contains the checkpoint for the [IT5 Large](https://huggingface.co/gsarti/it5-large) model fine-tuned on Formal-to-informal style transfer on the Italian subset of the XFORMAL 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
f2i = pipeline("text2text-generation", model='it5/it5-large-formal-to-informal')
f2i("Vi ringrazio infinitamente per vostra disponibilità")
>>> [{"generated_text": "e grazie per la vostra disponibilità!"}]
```
or loaded using autoclasses:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("it5/it5-large-formal-to-informal")
model = AutoModelForSeq2SeqLM.from_pretrained("it5/it5-large-formal-to-informal")
```
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}
}
``` |
gsarti/it5-small-formal-to-informal | gsarti | 2022-03-09T07:45:14Z | 16 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"italian",
"sequence-to-sequence",
"style-transfer",
"formality-style-transfer",
"it",
"dataset:yahoo/xformal_it",
"arxiv:2203.03759",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language:
- it
license: apache-2.0
tags:
- italian
- sequence-to-sequence
- style-transfer
- formality-style-transfer
datasets:
- yahoo/xformal_it
widget:
- text: "Questa performance è a dir poco spiacevole."
- text: "In attesa di un Suo cortese riscontro, Le auguriamo un piacevole proseguimento di giornata."
- text: "Questa visione mi procura una goduria indescrivibile."
- text: "qualora ciò possa interessarti, ti pregherei di contattarmi."
metrics:
- rouge
- bertscore
model-index:
- name: it5-small-formal-to-informal
results:
- task:
type: formality-style-transfer
name: "Formal-to-informal Style Transfer"
dataset:
type: xformal_it
name: "XFORMAL (Italian Subset)"
metrics:
- type: rouge1
value: 0.650
name: "Avg. Test Rouge1"
- type: rouge2
value: 0.450
name: "Avg. Test Rouge2"
- type: rougeL
value: 0.631
name: "Avg. Test RougeL"
- type: bertscore
value: 0.663
name: "Avg. 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: "8g"
source: "Google Cloud Platform Carbon Footprint"
training_type: "fine-tuning"
geographical_location: "Eemshaven, Netherlands, Europe"
hardware_used: "1 TPU v3-8 VM"
---
# IT5 Small for Formal-to-informal Style Transfer 🤗
This repository contains the checkpoint for the [IT5 Small](https://huggingface.co/gsarti/it5-small) model fine-tuned on Formal-to-informal style transfer on the Italian subset of the XFORMAL 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
f2i = pipeline("text2text-generation", model='it5/it5-small-formal-to-informal')
f2i("Vi ringrazio infinitamente per vostra disponibilità")
>>> [{"generated_text": "e grazie per la vostra disponibilità!"}]
```
or loaded using autoclasses:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("it5/it5-small-formal-to-informal")
model = AutoModelForSeq2SeqLM.from_pretrained("it5/it5-small-formal-to-informal")
```
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}
}
``` |
gsarti/mt5-base-formal-to-informal | gsarti | 2022-03-09T07:44:08Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"italian",
"sequence-to-sequence",
"style-transfer",
"formality-style-transfer",
"it",
"dataset:yahoo/xformal_it",
"arxiv:2203.03759",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language:
- it
license: apache-2.0
tags:
- italian
- sequence-to-sequence
- style-transfer
- formality-style-transfer
datasets:
- yahoo/xformal_it
widget:
- text: "Questa performance è a dir poco spiacevole."
- text: "In attesa di un Suo cortese riscontro, Le auguriamo un piacevole proseguimento di giornata."
- text: "Questa visione mi procura una goduria indescrivibile."
- text: "qualora ciò possa interessarti, ti pregherei di contattarmi."
metrics:
- rouge
- bertscore
model-index:
- name: mt5-base-formal-to-informal
results:
- task:
type: formality-style-transfer
name: "Formal-to-informal Style Transfer"
dataset:
type: xformal_it
name: "XFORMAL (Italian Subset)"
metrics:
- type: rouge1
value: 0.653
name: "Avg. Test Rouge1"
- type: rouge2
value: 0.449
name: "Avg. Test Rouge2"
- type: rougeL
value: 0.632
name: "Avg. Test RougeL"
- type: bertscore
value: 0.667
name: "Avg. 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: "40g"
source: "Google Cloud Platform Carbon Footprint"
training_type: "fine-tuning"
geographical_location: "Eemshaven, Netherlands, Europe"
hardware_used: "1 TPU v3-8 VM"
---
# mT5 Base for Formal-to-informal Style Transfer 🤗
This repository contains the checkpoint for the [mT5 Base](https://huggingface.co/google/mt5-base) model fine-tuned on Formal-to-informal style transfer on the Italian subset of the XFORMAL 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
f2i = pipeline("text2text-generation", model='it5/mt5-base-formal-to-informal')
f2i("Vi ringrazio infinitamente per vostra disponibilità")
>>> [{"generated_text": "e grazie per la vostra disponibilità!"}]
```
or loaded using autoclasses:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("it5/mt5-base-formal-to-informal")
model = AutoModelForSeq2SeqLM.from_pretrained("it5/mt5-base-formal-to-informal")
```
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 TBD},
url={TBD},
year={2022}
}
``` |
mrm8488/spanish-TinyBERT-betito-finetuned-xnli-es | mrm8488 | 2022-03-09T07:29:03Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:xnli",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-08T20:55:51Z | ---
tags:
- generated_from_trainer
datasets:
- xnli
metrics:
- accuracy
model-index:
- name: spanish-TinyBERT-betito-finetuned-xnli-es
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: xnli
type: xnli
args: es
metrics:
- name: Accuracy
type: accuracy
value: 0.7475049900199601
---
<!-- 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. -->
# spanish-TinyBERT-betito-finetuned-xnli-es
This model is a fine-tuned version of [mrm8488/spanish-TinyBERT-betito](https://huggingface.co/mrm8488/spanish-TinyBERT-betito) on the xnli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7104
- Accuracy: 0.7475
## 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: 2.50838112218154e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 13
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.7191 | 1.0 | 49399 | 0.6829 | 0.7112 |
| 0.6323 | 2.0 | 98798 | 0.6527 | 0.7305 |
| 0.5727 | 3.0 | 148197 | 0.6531 | 0.7465 |
| 0.4964 | 4.0 | 197596 | 0.7079 | 0.7427 |
| 0.4929 | 5.0 | 246995 | 0.7104 | 0.7475 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
M-Quan/wav2vec2-demo | M-Quan | 2022-03-09T06:20:54Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-09T01:26:39Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-demo
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-demo
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4239
- Wer: 0.3508
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4093 | 4.0 | 500 | 1.2405 | 0.8685 |
| 0.5597 | 8.0 | 1000 | 0.4538 | 0.4437 |
| 0.2113 | 12.0 | 1500 | 0.4106 | 0.3749 |
| 0.1188 | 16.0 | 2000 | 0.4609 | 0.3775 |
| 0.0776 | 20.0 | 2500 | 0.4239 | 0.3508 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.10.3
|
megagonlabs/cocosum-comm-self | megagonlabs | 2022-03-09T05:18:23Z | 0 | 0 | null | [
"license:bsd-3-clause",
"region:us"
] | null | 2022-03-07T23:31:25Z | ---
license: bsd-3-clause
---
See original GitHub repo for more details [here](https://github.com/megagonlabs/cocosum)
|
megagonlabs/cocosum-cont-few | megagonlabs | 2022-03-09T05:18:18Z | 0 | 0 | null | [
"license:bsd-3-clause",
"region:us"
] | null | 2022-03-07T23:29:46Z | ---
license: bsd-3-clause
---
See original GitHub repo for more details [here](https://github.com/megagonlabs/cocosum)
|
megagonlabs/cocosum-comm-few | megagonlabs | 2022-03-09T05:18:11Z | 0 | 0 | null | [
"license:bsd-3-clause",
"region:us"
] | null | 2022-03-07T23:32:02Z | ---
license: bsd-3-clause
---
See original GitHub repo for more details [here](https://github.com/megagonlabs/cocosum)
|
josu/albert-pt-br | josu | 2022-03-09T03:41:04Z | 6 | 1 | transformers | [
"transformers",
"pytorch",
"albert",
"fill-mask",
"portuguese",
"brazil",
"pt_BR",
"pt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-09T02:32:11Z | ---
language: pt
tags:
- portuguese
- brazil
- pt_BR
widget:
- text: Marte está no [MASK] solar.
---
``` python
from transformers import pipeline, AlbertTokenizer, AlbertForMaskedLM
model = AlbertForMaskedLM.from_pretrained('josu/albert-pt-br')
tokenizer = AlbertTokenizer.from_pretrained('josu/albert-pt-br')
unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer ,device=0)
text = 'Marte está no [MASK] solar.'
unmasker(text)
[{'score': 0.7004144191741943,
'token': 244,
'token_str': 'sistema',
'sequence': 'marte esta no sistema solar.'},
{'score': 0.02539917267858982,
'token': 4077,
'token_str': 'solar',
'sequence': 'marte esta no solar solar.'},
{'score': 0.020301498472690582,
'token': 49,
'token_str': 'seu',
'sequence': 'marte esta no seu solar.'},
{'score': 0.01753508299589157,
'token': 482,
'token_str': 'centro',
'sequence': 'marte esta no centro solar.'},
{'score': 0.013344300910830498,
'token': 1401,
'token_str': 'plano',
'sequence': 'marte esta no plano solar.'}]
``` |
jcai1/ss_ver1 | jcai1 | 2022-03-09T03:03:20Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-09T01:28:26Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: ss_ver1
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. -->
# ss_ver1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|
| No log | 1.0 | 436 | 0.0001 | 1.0 | 0.0 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
aaraki/distilbert-base-uncased-finetuned-cola | aaraki | 2022-03-09T02:08:47Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-09T01:56:17Z | ---
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: 0.40967417350821667
---
<!-- 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: 0.5026
- Matthews Correlation: 0.4097
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5335 | 1.0 | 535 | 0.5026 | 0.4097 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
KoichiYasuoka/roberta-base-ukrainian | KoichiYasuoka | 2022-03-08T23:33:19Z | 22 | 2 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ukrainian",
"masked-lm",
"ubertext",
"uk",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-08T23:25:41Z | ---
language:
- "uk"
tags:
- "ukrainian"
- "masked-lm"
- "ubertext"
license: "cc-by-sa-4.0"
pipeline_tag: "fill-mask"
mask_token: "[MASK]"
---
# roberta-base-ukrainian
## Model Description
This is a RoBERTa model pre-trained on [Корпус UberText](https://lang.org.ua/uk/corpora/#anchor4). You can fine-tune `roberta-base-ukrainian` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-base-ukrainian-upos), dependency-parsing, and so on.
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForMaskedLM
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-ukrainian")
model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-base-ukrainian")
```
|
BigSalmon/InformalToFormalLincoln25 | BigSalmon | 2022-03-08T23:17:13Z | 11 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:04Z | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln25")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln25")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
Text: failing to draw in the masses, the NBA has fallen into disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap solutions could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (California High-Speed Rail): built with an eye on the future, california's high-speed rail service resolves to change the face of travel.
Essay Intro (YIMBY's Need To Win): home to the most expensive housing market in the united states, san francisco is the city in which the yimby and anti-yimby hordes wage an eternal battle.
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
``` |
akozlo/lib_bal | akozlo | 2022-03-08T20:19:15Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-08T20:14:41Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: lib_balanced_gpt_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. -->
# lib_balanced_gpt_model
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
hello
|
z-uo/roberta-qasper | z-uo | 2022-03-08T18:40:11Z | 11 | 1 | transformers | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"question_answering",
"en",
"dataset:z-uo/qasper-squad",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-08T18:23:20Z | ---
language: en
tags:
- question_answering
datasets:
- z-uo/qasper-squad
---
# roberta-base for QA with qasper
Train from deepset/roberta-base-squad2.
How to use by python code:
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
# Load model with pipeline
model_name = "z-uo/roberta-qasper"
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
# Get predictions
QA_input = {
'question': 'what they propose?',
'context': "In this paper, we provide an innovative contribution in the research domain dedicated to crop mapping by exploiting the of Sentinel-2 satellite images time series, with the specific aim to extract information on 'where and when' crops are grown. The final goal is to set up a workflow able to reliably identify (classify) the different crops that are grown in a given area by exploiting an end-to-end (3+2)D convolutional neural network (CNN) for semantic segmentation. The method also has the ambition to provide information, at pixel level, regarding the period in which a given crop is cultivated during the season. To this end, we propose a solution called Class Activation Interval (CAI) which allows us to interpret, for each pixel, the reasoning made by CNN in the classification determining in which time interval, of the input time series, the class is likely to be present or not. Our experiments, using a public domain dataset, show that the approach is able to accurately detect crop classes with an overall accuracy of about 93% and that the network can detect discriminatory time intervals in which crop is cultivated. These results have twofold importance: (i) demonstrate the ability of the network to correctly interpret the investigated physical process (i.e., bare soil condition, plant growth, senescence and harvesting according to specific cultivated variety) and (ii) provide further information to the end-user (e.g., the presence of crops and its temporal dynamics)."
}
res = nlp(QA_input)
# Load model & tokenizer without pipeline
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
``` |
OrfeasTsk/bert-base-uncased-finetuned-triviaqa-large-batch | OrfeasTsk | 2022-03-08T18:35:17Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-07T20:17:41Z | { 'max_seq_length': 384,
'batch_size': 24,
'learning_rate': {'val': 3e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} |
OrfeasTsk/bert-base-uncased-finetuned-squadv2-large-batch | OrfeasTsk | 2022-03-08T18:34:54Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-08T18:23:33Z | { 'max_seq_length': 384,
'batch_size': 24,
'learning_rate': {'val': 3e-5, 'schelduler': 'Linear'},
'max_clip_norm': None,
'epochs': 2
} |
Ameer05/bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch-tweak-lr-8-100-1 | Ameer05 | 2022-03-08T16:43:01Z | 9 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | 2022-03-08T08:57:44Z | ---
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
|
gayanin/bart-med-term-mlm | gayanin | 2022-03-08T15:46:48Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-08T12:09:15Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-med-term-mlm
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-med-term-mlm
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2506
- Rouge2 Precision: 0.8338
- Rouge2 Recall: 0.6005
- Rouge2 Fmeasure: 0.6775
## 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.3426 | 1.0 | 15827 | 0.3029 | 0.8184 | 0.5913 | 0.6664 |
| 0.2911 | 2.0 | 31654 | 0.2694 | 0.8278 | 0.5963 | 0.6727 |
| 0.2571 | 3.0 | 47481 | 0.2549 | 0.8318 | 0.5985 | 0.6753 |
| 0.2303 | 4.0 | 63308 | 0.2506 | 0.8338 | 0.6005 | 0.6775 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
huggingtweets/feufillet-greatestquotes-hostagekiller | huggingtweets | 2022-03-08T13:28:29Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-08T13:26:49Z | ---
language: en
thumbnail: http://www.huggingtweets.com/feufillet-greatestquotes-hostagekiller/1646746104400/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1197820815636672513/JSCZmPDf_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1473236995497500675/FtwXDZld_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/378800000520968918/d38fd96468e9ba14c1f9f022eb0c4e61_400x400.png')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">sexy.funny.cute.pix & HUSSY2K. & Great Minds Quotes</div>
<div style="text-align: center; font-size: 14px;">@feufillet-greatestquotes-hostagekiller</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from sexy.funny.cute.pix & HUSSY2K. & Great Minds Quotes.
| Data | sexy.funny.cute.pix | HUSSY2K. | Great Minds Quotes |
| --- | --- | --- | --- |
| Tweets downloaded | 3091 | 3191 | 3200 |
| Retweets | 149 | 865 | 0 |
| Short tweets | 576 | 374 | 2 |
| Tweets kept | 2366 | 1952 | 3198 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3afdee2s/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 @feufillet-greatestquotes-hostagekiller's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/25fcmxer) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/25fcmxer/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/feufillet-greatestquotes-hostagekiller')
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)
|
jiobiala24/wav2vec2-base-cv-10000 | jiobiala24 | 2022-03-08T13:08:35Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-08T05:58:28Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-base-cv-10000
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-cv-10000
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-cv](https://huggingface.co/jiobiala24/wav2vec2-base-cv) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3393
- Wer: 0.3684
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4243 | 1.6 | 1000 | 0.7742 | 0.4210 |
| 0.3636 | 3.2 | 2000 | 0.8621 | 0.4229 |
| 0.2638 | 4.8 | 3000 | 0.9328 | 0.4094 |
| 0.2273 | 6.4 | 4000 | 0.9556 | 0.4087 |
| 0.187 | 8.0 | 5000 | 0.9093 | 0.4019 |
| 0.1593 | 9.6 | 6000 | 0.9842 | 0.4029 |
| 0.1362 | 11.2 | 7000 | 1.0651 | 0.4077 |
| 0.1125 | 12.8 | 8000 | 1.0550 | 0.3959 |
| 0.103 | 14.4 | 9000 | 1.1919 | 0.4002 |
| 0.0948 | 16.0 | 10000 | 1.1901 | 0.3983 |
| 0.0791 | 17.6 | 11000 | 1.1091 | 0.3860 |
| 0.0703 | 19.2 | 12000 | 1.2823 | 0.3904 |
| 0.0641 | 20.8 | 13000 | 1.2625 | 0.3817 |
| 0.057 | 22.4 | 14000 | 1.2821 | 0.3776 |
| 0.0546 | 24.0 | 15000 | 1.2975 | 0.3770 |
| 0.0457 | 25.6 | 16000 | 1.2998 | 0.3714 |
| 0.0433 | 27.2 | 17000 | 1.3574 | 0.3721 |
| 0.0423 | 28.8 | 18000 | 1.3393 | 0.3684 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
gayanin/t5-small-med-term-mlm | gayanin | 2022-03-08T11:46:57Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-08T07:26:59Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-small-med-term-mlm
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-med-term-mlm
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4736
- Rouge2 Precision: 0.7731
- Rouge2 Recall: 0.5541
- Rouge2 Fmeasure: 0.6251
## 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.6498 | 1.0 | 15827 | 0.5480 | 0.7629 | 0.5457 | 0.6161 |
| 0.5674 | 2.0 | 31654 | 0.4989 | 0.7697 | 0.551 | 0.622 |
| 0.5631 | 3.0 | 47481 | 0.4795 | 0.7726 | 0.5541 | 0.625 |
| 0.534 | 4.0 | 63308 | 0.4736 | 0.7731 | 0.5541 | 0.6251 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
SGrannemann/bert-finetuned-ner | SGrannemann | 2022-03-08T10:25:07Z | 4 | 0 | transformers | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-08T08:47:55Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: bert-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0225
- Validation Loss: 0.0519
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.0226 | 0.0519 | 0 |
| 0.0229 | 0.0519 | 1 |
| 0.0225 | 0.0519 | 2 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 1.18.4
- Tokenizers 0.11.6
|
brad1141/bert-finetuned-ner | brad1141 | 2022-03-08T09:31:59Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"longformer",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
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 [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6434
- Precision: 0.8589
- Recall: 0.8686
- F1: 0.8637
- Accuracy: 0.8324
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.615 | 1.0 | 1741 | 0.6111 | 0.8200 | 0.8652 | 0.8420 | 0.8046 |
| 0.4795 | 2.0 | 3482 | 0.5366 | 0.8456 | 0.8803 | 0.8626 | 0.8301 |
| 0.3705 | 3.0 | 5223 | 0.5412 | 0.8527 | 0.8786 | 0.8655 | 0.8339 |
| 0.2749 | 4.0 | 6964 | 0.5906 | 0.8559 | 0.8711 | 0.8634 | 0.8316 |
| 0.2049 | 5.0 | 8705 | 0.6434 | 0.8589 | 0.8686 | 0.8637 | 0.8324 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
anniezx/111 | anniezx | 2022-03-08T08:38:09Z | 0 | 0 | null | [
"license:artistic-2.0",
"region:us"
] | null | 2022-03-08T08:38:09Z | ---
license: artistic-2.0
---
|
zhiweitong/dpr-answer_encoder-single-nq-base | zhiweitong | 2022-03-08T07:25:05Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"dpr",
"feature-extraction",
"en",
"dataset:natural_questions",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-07T07:49:38Z | ---
language: en
datasets:
- natural_questions
---
# dpr-answer_encoder-single-nq-base
This encoder is used with [zhiweitong/dpr-ctx_encoder-single-nq-base](https://huggingface.co/zhiweitong/dpr-ctx_encoder-single-nq-base)
|
MikhailGalperin/distilbert-base-uncased-finetuned-ner | MikhailGalperin | 2022-03-08T06:49:43Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-07T20:29:52Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
model-index:
- name: distilbert-base-uncased-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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
huggingtweets/lilbratmia-littlehorney-plusbibi1 | huggingtweets | 2022-03-07T21:45:31Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-07T21:35:06Z | ---
language: en
thumbnail: http://www.huggingtweets.com/lilbratmia-littlehorney-plusbibi1/1646689525715/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1386970823681052680/oA_4HBKl_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1500892464772751365/6uhqt-Jx_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1483439308166123530/vKFDbs48_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Bibi und Anna & Vanny_Bunny™ & 💞 Mia 💞</div>
<div style="text-align: center; font-size: 14px;">@lilbratmia-littlehorney-plusbibi1</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Bibi und Anna & Vanny_Bunny™ & 💞 Mia 💞.
| Data | Bibi und Anna | Vanny_Bunny™ | 💞 Mia 💞 |
| --- | --- | --- | --- |
| Tweets downloaded | 1818 | 3230 | 3247 |
| Retweets | 9 | 503 | 134 |
| Short tweets | 341 | 343 | 1189 |
| Tweets kept | 1468 | 2384 | 1924 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/hm55g9hx/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 @lilbratmia-littlehorney-plusbibi1's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3dezdv7k) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3dezdv7k/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/lilbratmia-littlehorney-plusbibi1')
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)
|
espnet/Karthik_DSTC2_asr_train_asr_wav2vec_transformer | espnet | 2022-03-07T19:38:16Z | 1 | 0 | espnet | [
"espnet",
"tensorboard",
"audio",
"automatic-speech-recognition",
"en",
"dataset:sinhala",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2022-03-07T16:09:26Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- sinhala
license: cc-by-4.0
---
## ESPnet2 ASR pretrained model
### `espnet/Karthik_DSTC2_asr_train_asr_wav2vec_transformer`
This model was trained by Karthik using DSTC2/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
FinScience/FS-distilroberta-fine-tuned | FinScience | 2022-03-07T17:17:48Z | 61 | 2 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-07T11:29:41Z | ---
language:
- en
---
# FS-distilroberta-fine-tuned
The model was obtained by fine-tuning "mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis" model for sentiment analysis on financial news gathered by FinScience software. It predicts the sentiment of news with one label ("negative", "neutral" or "positive"). At the moment, the models works only in English.
## Training data
The training dataset consists of 2558 titles of news that were manually labelled by FinScience Team using doccano tool. A "neutral" label was assigned to those news for which an agreement was not reached. 70% (1790 news) of such dataset was employed as training set, while 15% (384) as validation set and the remaining 15% as test set. F1-score (macro) was selected as the evaluation metric.
| Set | Number of news | Scope |
| -------- | ----------------- | ----------------- |
| Training | 1790 | Training the model|
| Validation | 384 | Selecting the hyper-parameters |
| Test | 384 | Evaluating the performance|
## Accuracy
The table below summarizes the performance of the models that were tested on the same test set, consisting of 384 held-out titles:
| Language | Accuracy| F1-score (macro) |
| -------- | ---------------------- | ------------------- |
| FS-distilroberta-fine-tuned | 76%| 76%
|
Kevincp560/distilbart-cnn-12-3-finetuned-pubmed | Kevincp560 | 2022-03-07T15:55:27Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:pub_med_summarization_dataset",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-07T10:26:20Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pub_med_summarization_dataset
metrics:
- rouge
model-index:
- name: distilbart-cnn-12-3-finetuned-pubmed
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: pub_med_summarization_dataset
type: pub_med_summarization_dataset
args: document
metrics:
- name: Rouge1
type: rouge
value: 40.5642
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbart-cnn-12-3-finetuned-pubmed
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-3](https://huggingface.co/sshleifer/distilbart-cnn-12-3) on the pub_med_summarization_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1743
- Rouge1: 40.5642
- Rouge2: 16.9812
- Rougel: 25.3449
- Rougelsum: 36.46
- Gen Len: 141.95
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 2.469 | 1.0 | 4000 | 2.2956 | 38.3713 | 15.2594 | 23.6734 | 34.1634 | 141.707 |
| 2.2527 | 2.0 | 8000 | 2.1994 | 39.5939 | 16.2376 | 24.6363 | 35.5106 | 141.831 |
| 2.0669 | 3.0 | 12000 | 2.1780 | 40.078 | 16.6705 | 25.1119 | 35.9605 | 141.8475 |
| 1.9275 | 4.0 | 16000 | 2.1669 | 40.0825 | 16.6169 | 24.9702 | 36.0191 | 141.928 |
| 1.8102 | 5.0 | 20000 | 2.1743 | 40.5642 | 16.9812 | 25.3449 | 36.46 | 141.95 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
kenjis2542/mt5-small-finetuned-5k-th-to-en | kenjis2542 | 2022-03-07T14:11:40Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-07T12:49:31Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: mt5-small-finetuned-5k-th-to-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. -->
# mt5-small-finetuned-5k-th-to-en
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
spasis/distilbert-base-uncased-finetuned-imdb | spasis | 2022-03-07T13:50:20Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-07T13:32:31Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5173
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 40 | 2.5471 |
| No log | 2.0 | 80 | 2.4606 |
| No log | 3.0 | 120 | 2.5469 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
|
sanchit-gandhi/wav2vec2-2-gpt2-grid-search | sanchit-gandhi | 2022-03-07T13:18:03Z | 40 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
datasets:
- librispeech_asr
model-index:
- name: ''
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. -->
#
This model was trained from scratch on the librispeech_asr dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
|
gayanin/bart-mlm-paraphrasing | gayanin | 2022-03-07T12:37:38Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-07T11:50:34Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-mlm-paraphrasing
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-mlm-paraphrasing
This model is a fine-tuned version of [gayanin/bart-mlm-pubmed](https://huggingface.co/gayanin/bart-mlm-pubmed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4617
- Rouge2 Precision: 0.8361
- Rouge2 Recall: 0.6703
- Rouge2 Fmeasure: 0.7304
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.4845 | 1.0 | 1325 | 0.4270 | 0.8332 | 0.6701 | 0.7294 |
| 0.3911 | 2.0 | 2650 | 0.4195 | 0.8358 | 0.6713 | 0.7313 |
| 0.328 | 3.0 | 3975 | 0.4119 | 0.8355 | 0.6706 | 0.7304 |
| 0.2783 | 4.0 | 5300 | 0.4160 | 0.8347 | 0.6678 | 0.7284 |
| 0.2397 | 5.0 | 6625 | 0.4329 | 0.8411 | 0.6747 | 0.7351 |
| 0.2155 | 6.0 | 7950 | 0.4389 | 0.8382 | 0.6716 | 0.7321 |
| 0.1888 | 7.0 | 9275 | 0.4432 | 0.838 | 0.6718 | 0.7323 |
| 0.1724 | 8.0 | 10600 | 0.4496 | 0.8381 | 0.6714 | 0.7319 |
| 0.1586 | 9.0 | 11925 | 0.4575 | 0.8359 | 0.6704 | 0.7303 |
| 0.1496 | 10.0 | 13250 | 0.4617 | 0.8361 | 0.6703 | 0.7304 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
diwank/silicone-deberta-pair | diwank | 2022-03-07T08:43:13Z | 20 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"deberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: mit
---
# diwank/silicone-deberta-pair
`deberta-base`-based dialog acts classifier. Trained on the `balanced` variant of the [silicone-merged](https://huggingface.co/datasets/diwank/silicone-merged) dataset: a simplified merged dialog act data from datasets in the [silicone](https://huggingface.co/datasets/silicone) collection.
Takes two sentences as inputs (one previous and one current utterance of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. **Outputs one of 11 labels**:
```python
(0, 'acknowledge')
(1, 'answer')
(2, 'backchannel')
(3, 'reply_yes')
(4, 'exclaim')
(5, 'say')
(6, 'reply_no')
(7, 'hold')
(8, 'ask')
(9, 'intent')
(10, 'ask_yes_no')
```
## Example:
```python
from simpletransformers.classification import (
ClassificationModel, ClassificationArgs
)
model = ClassificationModel("deberta", "diwank/silicone-deberta-pair")
convert_to_label = lambda n: [
['acknowledge',
'answer',
'backchannel',
'reply_yes',
'exclaim',
'say',
'reply_no',
'hold',
'ask',
'intent',
'ask_yes_no'
][i] for i in n
]
predictions, raw_outputs = model.predict([["Say what is the meaning of life?", "I dont know"]])
convert_to_label(predictions) # answer
```
## Report from W&B
https://wandb.ai/diwank/da-silicone-combined/reports/silicone-deberta-pair--VmlldzoxNTczNjE5?accessToken=yj1jz4c365z0y5b3olgzye7qgsl7qv9lxvqhmfhtb6300hql6veqa5xiq1skn8ys |
AdapterHub/bioASQfactoid | AdapterHub | 2022-03-07T08:19:22Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"adapterhub:qa/bioasq",
"bart",
"region:us"
] | null | 2022-03-07T08:19:06Z | ---
tags:
- adapterhub:qa/bioasq
- adapter-transformers
- bart
---
# Adapter `AdapterHub/bioASQfactoid` for facebook/bart-base
An [adapter](https://adapterhub.ml) for the `facebook/bart-base` model that was trained on the [qa/bioasq](https://adapterhub.ml/explore/qa/bioasq/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("facebook/bart-base")
adapter_name = model.load_adapter("AdapterHub/bioASQfactoid", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> |
akshaychaudhary/distilbert-base-uncased-finetuned-devops-ner | akshaychaudhary | 2022-03-07T06:58:51Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-07T05:23:22Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-devops-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. -->
# distilbert-base-uncased-finetuned-devops-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6065
- Precision: 0.0254
- Recall: 0.1371
- F1: 0.0428
- Accuracy: 0.7637
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 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 | 144 | 0.8566 | 0.0300 | 0.1573 | 0.0503 | 0.7742 |
| No log | 2.0 | 288 | 1.3542 | 0.0283 | 0.1532 | 0.0477 | 0.7641 |
| No log | 3.0 | 432 | 1.6065 | 0.0254 | 0.1371 | 0.0428 | 0.7637 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
cammy/bart-large-cnn-finetuned-weaksup-1000-pad-early-new1 | cammy | 2022-03-07T06:18:16Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-07T06:01:26Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-weaksup-1000-pad-early-new1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-weaksup-1000-pad-early-new1
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4948
- Rouge1: 28.1465
- Rouge2: 13.4076
- Rougel: 22.2763
- Rougelsum: 25.2087
- Gen Len: 68.58
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.156 | 1.0 | 1000 | 0.4377 | 27.8782 | 13.1274 | 21.2329 | 24.6465 | 66.25 |
| 0.0843 | 2.0 | 2000 | 0.4948 | 28.1465 | 13.4076 | 22.2763 | 25.2087 | 68.58 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
billfrench/autonlp-cyberlandr-ai-4-614417501 | billfrench | 2022-03-07T00:57:12Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:billfrench/autonlp-data-cyberlandr-ai-4",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-07T00:54:15Z | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- billfrench/autonlp-data-cyberlandr-ai-4
co2_eq_emissions: 1.6912535041856878
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 614417501
- CO2 Emissions (in grams): 1.6912535041856878
## Validation Metrics
- Loss: 1.305419921875
- Accuracy: 0.5
- Macro F1: 0.3333333333333333
- Micro F1: 0.5
- Weighted F1: 0.4444444444444444
- Macro Precision: 0.375
- Micro Precision: 0.5
- Weighted Precision: 0.5
- Macro Recall: 0.375
- Micro Recall: 0.5
- Weighted Recall: 0.5
## 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/billfrench/autonlp-cyberlandr-ai-4-614417501
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("billfrench/autonlp-cyberlandr-ai-4-614417501", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("billfrench/autonlp-cyberlandr-ai-4-614417501", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
billfrench/autonlp-cyberlandr-ai-4-614417500 | billfrench | 2022-03-07T00:56:09Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:billfrench/autonlp-data-cyberlandr-ai-4",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-07T00:54:24Z | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- billfrench/autonlp-data-cyberlandr-ai-4
co2_eq_emissions: 1.131603488976132
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 614417500
- CO2 Emissions (in grams): 1.131603488976132
## Validation Metrics
- Loss: 1.4588216543197632
- Accuracy: 0.3333333333333333
- Macro F1: 0.225
- Micro F1: 0.3333333333333333
- Weighted F1: 0.2333333333333333
- Macro Precision: 0.1875
- Micro Precision: 0.3333333333333333
- Weighted Precision: 0.20833333333333334
- Macro Recall: 0.375
- Micro Recall: 0.3333333333333333
- Weighted Recall: 0.3333333333333333
## 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/billfrench/autonlp-cyberlandr-ai-4-614417500
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("billfrench/autonlp-cyberlandr-ai-4-614417500", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("billfrench/autonlp-cyberlandr-ai-4-614417500", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
meame2010/rare-puppers | meame2010 | 2022-03-07T00:03:06Z | 68 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2022-03-07T00:02:57Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.644444465637207
---
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### dog drinking water
#### dog eating food
#### dog playing toy
#### dog sleeping
 |
Ayham/roberta_ernie_summarization_cnn_dailymail | Ayham | 2022-03-06T22:01:31Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-06T14:27:11Z | ---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: roberta_ernie_summarization_cnn_dailymail
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_ernie_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Kuray107/swbd-5percent-supervised | Kuray107 | 2022-03-06T16:14:11Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-05T15:36:19Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: swbd-5percent-supervised
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. -->
# swbd-5percent-supervised
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6970
- Wer: 0.1352
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 6.8534 | 0.64 | 1000 | 2.9535 | 1.0 |
| 1.8605 | 1.28 | 2000 | 0.7878 | 0.3719 |
| 0.9862 | 1.92 | 3000 | 0.5906 | 0.2684 |
| 0.8405 | 2.56 | 4000 | 0.5555 | 0.2151 |
| 0.6972 | 3.2 | 5000 | 0.5905 | 0.1992 |
| 0.6033 | 3.84 | 6000 | 0.4867 | 0.1781 |
| 0.5393 | 4.48 | 7000 | 0.5447 | 0.1805 |
| 0.529 | 5.12 | 8000 | 0.5398 | 0.1746 |
| 0.5072 | 5.77 | 9000 | 0.5093 | 0.1706 |
| 0.4331 | 6.41 | 10000 | 0.4990 | 0.1627 |
| 0.4837 | 7.05 | 11000 | 0.5319 | 0.1634 |
| 0.3867 | 7.69 | 12000 | 0.4866 | 0.1595 |
| 0.345 | 8.33 | 13000 | 0.5202 | 0.1582 |
| 0.372 | 8.97 | 14000 | 0.5396 | 0.1547 |
| 0.355 | 9.61 | 15000 | 0.5992 | 0.1493 |
| 0.3258 | 10.25 | 16000 | 0.5247 | 0.1527 |
| 0.3327 | 10.89 | 17000 | 0.5664 | 0.1512 |
| 0.3422 | 11.53 | 18000 | 0.5819 | 0.1456 |
| 0.2815 | 12.17 | 19000 | 0.5692 | 0.1453 |
| 0.2719 | 12.81 | 20000 | 0.5012 | 0.1476 |
| 0.2838 | 13.45 | 21000 | 0.5286 | 0.1454 |
| 0.2418 | 14.09 | 22000 | 0.6238 | 0.1486 |
| 0.2412 | 14.73 | 23000 | 0.5889 | 0.1456 |
| 0.2227 | 15.37 | 24000 | 0.5901 | 0.1459 |
| 0.2129 | 16.02 | 25000 | 0.5959 | 0.1454 |
| 0.2071 | 16.66 | 26000 | 0.6259 | 0.1427 |
| 0.2185 | 17.3 | 27000 | 0.6581 | 0.1437 |
| 0.1982 | 17.94 | 28000 | 0.6194 | 0.1411 |
| 0.1928 | 18.58 | 29000 | 0.5940 | 0.1409 |
| 0.1885 | 19.22 | 30000 | 0.6733 | 0.1417 |
| 0.1835 | 19.86 | 31000 | 0.6363 | 0.1393 |
| 0.1756 | 20.5 | 32000 | 0.6675 | 0.1382 |
| 0.1776 | 21.14 | 33000 | 0.6147 | 0.1407 |
| 0.1758 | 21.78 | 34000 | 0.6405 | 0.1420 |
| 0.1645 | 22.42 | 35000 | 0.6999 | 0.1401 |
| 0.1631 | 23.06 | 36000 | 0.6224 | 0.1385 |
| 0.1494 | 23.7 | 37000 | 0.6639 | 0.1374 |
| 0.1472 | 24.34 | 38000 | 0.6471 | 0.1373 |
| 0.1514 | 24.98 | 39000 | 0.6570 | 0.1395 |
| 0.1527 | 25.62 | 40000 | 0.6876 | 0.1375 |
| 0.1514 | 26.27 | 41000 | 0.6835 | 0.1376 |
| 0.1344 | 26.91 | 42000 | 0.6987 | 0.1372 |
| 0.1267 | 27.55 | 43000 | 0.7026 | 0.1362 |
| 0.1384 | 28.19 | 44000 | 0.7021 | 0.1366 |
| 0.1264 | 28.83 | 45000 | 0.7016 | 0.1355 |
| 0.1227 | 29.47 | 46000 | 0.6970 | 0.1352 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.2
- Datasets 1.18.2
- Tokenizers 0.10.3
|
smartiros/Silva_TEST | smartiros | 2022-03-06T15:46:41Z | 4 | 0 | transformers | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-06T15:46:28Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: tmplujkwod0
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tmplujkwod0
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5292
- Train Accuracy: 0.875
- Validation Loss: 0.5870
- Validation Accuracy: 0.5
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6565 | 0.625 | 0.7534 | 0.5 | 0 |
| 0.5292 | 0.875 | 0.5870 | 0.5 | 1 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Tokenizers 0.11.6
|
crabz/distil-slovakbert-ner | crabz | 2022-03-06T12:40:16Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"autotrain_compatible",
"region:us"
] | token-classification | 2022-03-06T12:17:02Z | ---
tags:
- generated_from_trainer
datasets:
- wikiann
inference: false
model-index:
- name: distil-slovakbert-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. -->
# distil-slovakbert-ner
This model is a fine-tuned version of [crabz/distil-slovakbert](https://huggingface.co/crabz/distil-slovakbert) on the wikiann sk dataset.
- F1: 0.9307
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 1.15.1
- Tokenizers 0.11.0
|
crabz/distil-slovakbert-upos | crabz | 2022-03-06T12:38:56Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:universal_dependencies",
"model-index",
"autotrain_compatible",
"region:us"
] | token-classification | 2022-03-05T19:42:43Z | ---
tags:
- generated_from_trainer
datasets:
- universal_dependencies
metrics:
- precision
- recall
- f1
- accuracy
inference: false
model-index:
- name: distil-slovakbert-upos
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: universal_dependencies sk_snk
type: universal_dependencies
args: sk_snk
metrics:
- name: Precision
type: precision
value: 0.9771104035797263
- name: Recall
type: recall
value: 0.9785418821096173
- name: F1
type: f1
value: 0.9778256189451022
- name: Accuracy
type: accuracy
value: 0.9800851200513933
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distil-slovakbert-upos
This model is a fine-tuned version of [crabz/distil-slovakbert](https://huggingface.co/crabz/distil-slovakbert) on the universal_dependencies sk_snk dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1207
- Precision: 0.9771
- Recall: 0.9785
- F1: 0.9778
- Accuracy: 0.9801
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 266 | 0.2168 | 0.9570 | 0.9554 | 0.9562 | 0.9610 |
| 0.3935 | 2.0 | 532 | 0.1416 | 0.9723 | 0.9736 | 0.9730 | 0.9740 |
| 0.3935 | 3.0 | 798 | 0.1236 | 0.9722 | 0.9735 | 0.9728 | 0.9747 |
| 0.0664 | 4.0 | 1064 | 0.1195 | 0.9722 | 0.9741 | 0.9732 | 0.9766 |
| 0.0664 | 5.0 | 1330 | 0.1160 | 0.9764 | 0.9772 | 0.9768 | 0.9789 |
| 0.0377 | 6.0 | 1596 | 0.1194 | 0.9763 | 0.9776 | 0.9770 | 0.9790 |
| 0.0377 | 7.0 | 1862 | 0.1188 | 0.9740 | 0.9755 | 0.9748 | 0.9777 |
| 0.024 | 8.0 | 2128 | 0.1188 | 0.9762 | 0.9777 | 0.9769 | 0.9793 |
| 0.024 | 9.0 | 2394 | 0.1207 | 0.9774 | 0.9789 | 0.9781 | 0.9802 |
| 0.0184 | 10.0 | 2660 | 0.1207 | 0.9771 | 0.9785 | 0.9778 | 0.9801 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.11.0
|
crabz/slovakbert-upos | crabz | 2022-03-06T12:31:41Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"license:mit",
"autotrain_compatible",
"region:us"
] | token-classification | 2022-03-05T16:47:22Z | ---
license: mit
inference: false
---
|
crabz/distil-slovakbert | crabz | 2022-03-06T12:30:11Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"sk",
"dataset:c4-sk",
"license:mit",
"autotrain_compatible",
"region:us"
] | fill-mask | 2022-03-04T15:42:01Z | ---
language: sk
license: mit
tags:
- fill-mask
- roberta
datasets:
- c4-sk
inference: false
---
|
AG/pretraining | AG | 2022-03-06T12:27:50Z | 17 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-02T23:29:04Z | Pre trained on clus_ chapter only. |
nandinib1999/quote-generator | nandinib1999 | 2022-03-06T12:04:44Z | 68 | 3 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"text generation",
"en",
"dataset:quotes-500K",
"license:cc",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language:
- en
thumbnail:
tags:
- text generation
license: cc
datasets:
- quotes-500K
metrics:
- perplexity
---
# Quotes Generator
## Model description
This is a GPT2 model fine-tuned on the Quotes-500K dataset.
## Intended uses & limitations
For a given user prompt, it can generate motivational quotes starting with it.
#### How to use
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("nandinib1999/quote-generator")
model = AutoModelWithLMHead.from_pretrained("nandinib1999/quote-generator")
```
## Training data
This is the distribution of the total dataset into training, validation and test dataset for the fine-tuning task.
<table style="width:30%">
<tr>
<th>train</th>
<td>349796</td>
</tr>
<tr>
<th>validation</th>
<td>99942</td>
</tr>
<tr>
<th>test</th>
<td>49971</td>
</tr>
</table>
## Training procedure
The model was fine-tuned using the Google Colab GPU for one epoch. The weights of the pre-trained GPT2 model were used as a base.
## Eval results
<table style="width:30%">
<tr>
<th>Epoch</th>
<th>Perplexity</th>
</tr>
<tr>
<td>1</td>
<td>15.180</td>
</tr>
</table> |
Kuray107/librispeech-100h-supervised | Kuray107 | 2022-03-06T08:07:22Z | 13 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:04Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: librispeech-100h-supervised
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
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0955
- Wer: 0.0345
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.8277 | 0.42 | 500 | 2.9071 | 1.0 |
| 2.0261 | 0.84 | 1000 | 0.3060 | 0.2496 |
| 0.2181 | 1.26 | 1500 | 0.1172 | 0.0873 |
| 0.1255 | 1.68 | 2000 | 0.0894 | 0.0637 |
| 0.0971 | 2.1 | 2500 | 0.0821 | 0.0560 |
| 0.078 | 2.52 | 3000 | 0.0751 | 0.0500 |
| 0.0706 | 2.94 | 3500 | 0.0721 | 0.0456 |
| 0.0609 | 3.36 | 4000 | 0.0755 | 0.0464 |
| 0.0572 | 3.78 | 4500 | 0.0705 | 0.0431 |
| 0.0528 | 4.2 | 5000 | 0.0715 | 0.0423 |
| 0.0481 | 4.62 | 5500 | 0.0691 | 0.0403 |
| 0.0471 | 5.04 | 6000 | 0.0743 | 0.0401 |
| 0.0412 | 5.46 | 6500 | 0.0757 | 0.0399 |
| 0.0416 | 5.88 | 7000 | 0.0688 | 0.0378 |
| 0.0391 | 6.3 | 7500 | 0.0704 | 0.0383 |
| 0.0367 | 6.72 | 8000 | 0.0742 | 0.0387 |
| 0.0349 | 7.14 | 8500 | 0.0732 | 0.0388 |
| 0.033 | 7.56 | 9000 | 0.0719 | 0.0374 |
| 0.0327 | 7.98 | 9500 | 0.0750 | 0.0369 |
| 0.0292 | 8.4 | 10000 | 0.0734 | 0.0368 |
| 0.0303 | 8.82 | 10500 | 0.0733 | 0.0365 |
| 0.0283 | 9.24 | 11000 | 0.0766 | 0.0357 |
| 0.0269 | 9.66 | 11500 | 0.0761 | 0.0350 |
| 0.0268 | 10.08 | 12000 | 0.0802 | 0.0359 |
| 0.0245 | 10.42 | 12500 | 0.0758 | 0.0354 |
| 0.023 | 10.84 | 13000 | 0.0775 | 0.0349 |
| 0.0186 | 11.26 | 13500 | 0.0817 | 0.0355 |
| 0.0176 | 11.68 | 14000 | 0.0853 | 0.0354 |
| 0.0163 | 12.1 | 14500 | 0.0880 | 0.0347 |
| 0.0156 | 12.52 | 15000 | 0.0864 | 0.0357 |
| 0.0141 | 12.94 | 15500 | 0.0897 | 0.0355 |
| 0.0134 | 13.36 | 16000 | 0.0915 | 0.0349 |
| 0.013 | 13.78 | 16500 | 0.0928 | 0.0350 |
| 0.0097 | 13.42 | 17000 | 0.0955 | 0.0345 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.2
- Datasets 1.18.2
- Tokenizers 0.10.3
|
clisi2000/distilbert-base-uncased-finetuned-emotion | clisi2000 | 2022-03-06T07:09:00Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-05T04:03:14Z | ---
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: 0.9245
- name: F1
type: f1
value: 0.9246284188099615
---
<!-- 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: 0.2183
- Accuracy: 0.9245
- F1: 0.9246
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8174 | 1.0 | 250 | 0.3166 | 0.905 | 0.9023 |
| 0.2534 | 2.0 | 500 | 0.2183 | 0.9245 | 0.9246 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.2+cpu
- Datasets 1.16.1
- Tokenizers 0.10.1
|
Kuray107/librispeech-5h-supervised | Kuray107 | 2022-03-06T06:43:53Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-05T23:00:11Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: librispeech-5h-supervised
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-5h-supervised
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2041
- Wer: 0.0624
## 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: 16
- 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: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.7758 | 11.11 | 1000 | 0.3120 | 0.2337 |
| 0.1238 | 22.22 | 2000 | 0.1651 | 0.0826 |
| 0.0383 | 33.33 | 3000 | 0.1667 | 0.0712 |
| 0.023 | 44.44 | 4000 | 0.1893 | 0.0685 |
| 0.0166 | 55.56 | 5000 | 0.2008 | 0.0666 |
| 0.0131 | 66.67 | 6000 | 0.1942 | 0.0639 |
| 0.0106 | 77.78 | 7000 | 0.1979 | 0.0628 |
| 0.0091 | 88.89 | 8000 | 0.2027 | 0.0628 |
| 0.008 | 100.0 | 9000 | 0.2041 | 0.0624 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.2
- Datasets 1.18.2
- Tokenizers 0.10.3
|
ttmusic/distilbert-base-uncased-finetuned-imdb | ttmusic | 2022-03-06T01:28:38Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-06T01:17:45Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4513
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 79 | 2.5347 |
| 2.6681 | 2.0 | 158 | 2.4416 |
| 2.6681 | 3.0 | 237 | 2.4634 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.6
|
xinzhel/gpt2-ag-news | xinzhel | 2022-03-06T00:08:03Z | 33 | 1 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-05T04:44:40Z | ---
license: apache-2.0
---
|
BigSalmon/Points3 | BigSalmon | 2022-03-05T22:03:31Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-05T21:39:49Z | Example Prompt:
```
###
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
###
-
``` |
mp6kv/main_intent_test | mp6kv | 2022-03-05T19:18:02Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-05T17:22:41Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: main_intent_test
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. -->
# main_intent_test
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
## Model description
Custom data generated labeling text according to these five categories.
Five categories represent the five essential intents of a user for the ACTS scenario.
- Connect : Greetings and introduction with the student
- Pump : Asking the student for information
- Inform : Providing information to the student
- Feedback : Praising the student (positive feedback) or informing the student they are not on the right path (negative feedback)
- None : Not related to scenario
Takes a user input of string text and classifies it according to one of five categories.
## Intended uses & limitations
from transformers import pipeline
classifier = pipeline("text-classification",model="mp6kv/main_intent_test")
output = classifier("great job, you're getting it!")
score = output[0]['score']
label = output[0]['label']
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
batterydata/batteryscibert-uncased | batterydata | 2022-03-05T16:14:28Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:batterypapers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- batterypapers
---
# BatterySciBERT-uncased model
Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the [SciBERT-uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) weights. It was introduced in
[this paper](paper_link) and first released in
[this repository](https://github.com/ShuHuang/batterybert). This model is uncased: it does not make a difference
between english and English.
## Model description
BatterySciBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the [SciBERT-uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) weights. This means
it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model
randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict
the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one
after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to
learn a bidirectional representation of the sentence.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Training data
The BatterySciBERT model was pretrained on the full text of battery papers only, after initialized from the [SciBERT-uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at [Github](https://github.com/ShuHuang/batterybert/blob/main/corpus.txt).
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 31,090. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
### Pretraining
The model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
learning rate warmup for 10,000 steps and linear decay of the learning rate after.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
See the [model hub](https://huggingface.co/models?filter=batterybert) to look for fine-tuned versions on a task that
interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='batterydata/batteryscibert-uncased')
>>> unmasker("Hello I'm a <mask> model.")
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('batterydata/batteryscibert-uncased')
model = BertModel.from_pretrained('batterydata/batteryscibert-uncased')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('batterydata/batteryscibert-uncased')
model = TFBertModel.from_pretrained('batterydata/batteryscibert-uncased')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
Final loss: 1.095.
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
|
batterydata/batteryscibert-cased | batterydata | 2022-03-05T16:11:45Z | 14 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:batterypapers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- batterypapers
---
# BatterySciBERT-cased model
Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the [SciBERT-cased](https://huggingface.co/allenai/scibert_scivocab_cased) weights. It was introduced in
[this paper](paper_link) and first released in
[this repository](https://github.com/ShuHuang/batterybert). This model is case-sensitive: it makes a difference between english and English.
## Model description
BatterySciBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the [SciBERT-cased](https://huggingface.co/allenai/scibert_scivocab_cased) weights. This means
it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model
randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict
the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one
after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to
learn a bidirectional representation of the sentence.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Training data
The BatterySciBERT model was pretrained on the full text of battery papers only, after initialized from the [SciBERT-cased](https://huggingface.co/allenai/scibert_scivocab_cased) weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at [Github](https://github.com/ShuHuang/batterybert/blob/main/corpus.txt).
## Training procedure
### Preprocessing
The texts are tokenized using WordPiece and a vocabulary size of 31,116. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
### Pretraining
The model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
learning rate warmup for 10,000 steps and linear decay of the learning rate after.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
See the [model hub](https://huggingface.co/models?filter=batterybert) to look for fine-tuned versions on a task that
interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='batterydata/batteryscibert-cased')
>>> unmasker("Hello I'm a <mask> model.")
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('batterydata/batteryscibert-cased')
model = BertModel.from_pretrained('batterydata/batteryscibert-cased')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('batterydata/batteryscibert-cased')
model = TFBertModel.from_pretrained('batterydata/batteryscibert-cased')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
Final loss: 1.0505.
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
|
batterydata/batteryonlybert-cased-abstract | batterydata | 2022-03-05T14:54:53Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"en",
"dataset:batterydata/paper-abstracts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BatteryOnlyBERT-cased for Battery Abstract Classification
**Language model:** batteryonlybert-cased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 32
n_epochs = 14
base_LM_model = "batteryonlybert-cased"
learning_rate = 2e-5
```
## Performance
```
"Validation accuracy": 97.33,
"Test accuracy": 97.34,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model_name = "batterydata/batteryonlybert-cased-abstract"
# a) Get predictions
nlp = pipeline('text-classification', model=model_name, tokenizer=model_name)
input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'}
res = nlp(input)
# b) Load model & tokenizer
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement |
batterydata/batteryonlybert-uncased-abstract | batterydata | 2022-03-05T14:53:56Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"en",
"dataset:batterydata/paper-abstracts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BatteryOnlyBERT-uncased for Battery Abstract Classification
**Language model:** batteryonlybert-uncased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 16
n_epochs = 13
base_LM_model = "batteryonlybert-uncased"
learning_rate = 3e-5
```
## Performance
```
"Validation accuracy": 97.18,
"Test accuracy": 97.08,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model_name = "batterydata/batteryonlybert-uncased-abstract"
# a) Get predictions
nlp = pipeline('text-classification', model=model_name, tokenizer=model_name)
input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'}
res = nlp(input)
# b) Load model & tokenizer
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement |
batterydata/bert-base-uncased-abstract | batterydata | 2022-03-05T14:44:13Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"Text Classification",
"en",
"dataset:batterydata/paper-abstracts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
language: en
tags: Text Classification
license: apache-2.0
datasets:
- batterydata/paper-abstracts
metrics: glue
---
# BERT-base-uncased for Battery Abstract Classification
**Language model:** bert-base-uncased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 32
n_epochs = 13
base_LM_model = "bert-base-uncased"
learning_rate = 2e-5
```
## Performance
```
"Validation accuracy": 96.79,
"Test accuracy": 96.29,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model_name = "batterydata/bert-base-uncased-abstract"
# a) Get predictions
nlp = pipeline('text-classification', model=model_name, tokenizer=model_name)
input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'}
res = nlp(input)
# b) Load model & tokenizer
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement |
batterydata/batterybert-cased-squad-v1 | batterydata | 2022-03-05T13:50:54Z | 5,999 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"question answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
language: en
tags: question answering
license: apache-2.0
datasets:
- squad
- batterydata/battery-device-data-qa
metrics: squad
---
# BatteryBERT-cased for QA
**Language model:** batterybert-cased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 16
n_epochs = 4
base_LM_model = "batterybert-cased"
max_seq_len = 386
learning_rate = 2e-5
doc_stride=128
max_query_length=64
```
## Performance
Evaluated on the SQuAD v1.0 dev set.
```
"exact": 81.54,
"f1": 89.16,
```
Evaluated on the battery device dataset.
```
"precision": 70.74,
"recall": 84.19,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "batterydata/batterybert-cased-squad-v1"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'What is the electrolyte?',
'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
|
elena-soare/t5-base-datasaur | elena-soare | 2022-03-05T13:18:15Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-05T13:17:43Z | T5-base model pre-trained on e-commerce data. |
Kevincp560/distilbart-xsum-12-1-finetuned-pubmed | Kevincp560 | 2022-03-05T00:06:55Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:pub_med_summarization_dataset",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-04T18:48:32Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pub_med_summarization_dataset
metrics:
- rouge
model-index:
- name: distilbart-xsum-12-1-finetuned-pubmed
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: pub_med_summarization_dataset
type: pub_med_summarization_dataset
args: document
metrics:
- name: Rouge1
type: rouge
value: 27.0012
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbart-xsum-12-1-finetuned-pubmed
This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-1](https://huggingface.co/sshleifer/distilbart-xsum-12-1) on the pub_med_summarization_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8236
- Rouge1: 27.0012
- Rouge2: 12.728
- Rougel: 19.8685
- Rougelsum: 25.0485
- Gen Len: 59.969
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 3.3604 | 1.0 | 4000 | 3.1575 | 25.0078 | 11.5381 | 18.4246 | 23.1605 | 54.8935 |
| 3.0697 | 2.0 | 8000 | 2.9478 | 26.4947 | 12.5411 | 19.4328 | 24.6123 | 57.948 |
| 2.8638 | 3.0 | 12000 | 2.8672 | 26.8856 | 12.7568 | 19.8949 | 24.8745 | 59.6245 |
| 2.7243 | 4.0 | 16000 | 2.8347 | 26.7347 | 12.5152 | 19.6516 | 24.7756 | 60.439 |
| 2.6072 | 5.0 | 20000 | 2.8236 | 27.0012 | 12.728 | 19.8685 | 25.0485 | 59.969 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
Ayham/ernie_ernie_summarization_cnn_dailymail | Ayham | 2022-03-04T20:54:42Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-04T14:48:41Z | ---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: ernie_ernie_summarization_cnn_dailymail
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. -->
# ernie_ernie_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer_wav2vec2 | espnet | 2022-03-04T20:51:40Z | 1 | 0 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:swbd_sentiment",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2022-03-04T20:49:57Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- swbd_sentiment
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer_wav2vec2`
This model was trained by YushiUeda using swbd_sentiment recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout e5c0e0dbdab7e56ea9bf0a852bac10a1d99acf64
pip install -e .
cd egs2/swbd_sentiment/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer_wav2vec2
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Fri Mar 4 07:57:13 EST 2022`
- python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.9.0+cu102`
- Git hash: `3b53aedc654fd30a828689c2139a1e130adac077`
- Commit date: `Fri Feb 25 00:13:16 2022 -0500`
## Using Conformer based encoder, Transformer based decoder and self-supervised learning features (Wav2vec2.0) with spectral augmentation and predicting transcript along with sentiment
- ASR config: [conf/tuning/train_asr_conformer_wav2vec2.yaml](conf/tuning/train_asr_conformer_wav2vec2.yaml)
- token_type: word
- labels: Positive, Neutral, Negative
|dataset|Snt|Intent Classification Macro F1 (%)| Weighted F1 (%)| Micro F1 (%)|
|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave_10best/valid|2415|64.5|67.5|67.4|
|decode_asr_asr_model_valid.acc.ave_10best/test|2438|64.1|66.5|66.3|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_conformer_wav2vec2.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer_wav2vec2_raw_en_word
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 57795
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
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best_model_criterion:
- - valid
- acc
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keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 3
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param:
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num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 40000000
valid_batch_bins: null
train_shape_file:
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- exp/asr_stats_raw_en_word/train/text_shape.word
valid_shape_file:
- exp/asr_stats_raw_en_word/valid/speech_shape
- exp/asr_stats_raw_en_word/valid/text_shape.word
batch_type: numel
valid_batch_type: null
fold_length:
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sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
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- speech
- sound
- - dump/raw/train/text
- text
- text
valid_data_path_and_name_and_type:
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- speech
- sound
- - dump/raw/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.0025
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
token_list:
- <blank>
- <unk>
- i
- and
- the
- you
- that
- it
- a
- Neutral
- to
- uh
- '''s'
- of
- know
- Positive
- they
- in
- we
- '''t'
- have
- but
- so
- was
- like
- Negative
- yeah
- is
- just
- um
- well
- do
- for
- think
- don
- there
- or
- 'on'
- '''re'
- my
- what
- really
- be
- with
- not
- if
- are
- one
- he
- '''ve'
- because
- '''m'
- about
- all
- get
- can
- had
- out
- at
- them
- when
- this
- as
- oh
- lot
- up
- people
- some
- then
- would
- go
- right
- mean
- now
- time
- kind
- got
- going
- good
- she
- things
- more
- were
- from
- something
- been
- 'no'
- see
- me
- too
- an
- your
- much
- little
- guess
- how
- where
- our
- very
- here
- their
- thing
- two
- '''ll'
- other
- did
- years
- work
- even
- has
- any
- way
- probably
- those
- could
- say
- real
- back
- '''d'
- year
- down
- home
- than
- want
- didn
- into
- pretty
- okay
- who
- take
- huh
- school
- said
- make
- over
- kids
- never
- always
- put
- by
- her
- stuff
- went
- doing
- three
- these
- 'yes'
- which
- around
- only
- big
- maybe
- 'off'
- anything
- day
- t
- sure
- actually
- come
- money
- him
- different
- everything
- still
- used
- many
- five
- will
- sort
- nice
- us
- last
- his
- thought
- every
- most
- getting
- first
- feel
- bit
- need
- children
- same
- course
- also
- new
- care
- family
- hum
- long
- through
- before
- use
- done
- should
- house
- old
- let
- does
- car
- being
- problem
- doesn
- four
- seems
- though
- pay
- look
- whole
- great
- husband
- haven
- try
- live
- trying
- ever
- why
- read
- better
- find
- far
- keep
- ago
- sometimes
- watch
- interesting
- quite
- area
- hard
- talking
- else
- another
- part
- bad
- having
- twenty
- whatever
- place
- couple
- usually
- 'true'
- high
- texas
- seen
- fact
- s
- enough
- after
- own
- college
- while
- country
- hundred
- somebody
- few
- either
- times
- week
- away
- gonna
- type
- job
- six
- dollars
- tell
- might
- remember
- again
- came
- give
- started
- start
- ten
- made
- play
- able
- dallas
- enjoy
- working
- once
- c
- someone
- life
- least
- v
- everybody
- since
- fun
- both
- talk
- wouldn
- ones
- news
- anyway
- wasn
- person
- heard
- believe
- am
- th
- buy
- may
- point
- call
- night
- y
- almost
- bye
- isn
- system
- wanted
- called
- took
- state
- wife
- child
- half
- women
- goes
- next
- yet
- especially
- love
- looking
- parents
- gone
- such
- gets
- understand
- together
- movie
- until
- w
- days
- end
- saying
- idea
- saw
- music
- mother
- thirty
- couldn
- makes
- stay
- change
- m
- basically
- wonderful
- problems
- guy
- worked
- spend
- help
- lived
- credit
- whether
- seem
- eight
- n
- best
- world
- run
- hear
- bought
- young
- each
- months
- seven
- places
- supposed
- city
- matter
- coming
- exactly
- d
- small
- summer
- comes
- certain
- company
- less
- thinking
- won
- during
- b
- thousand
- agree
- show
- daughter
- sounds
- myself
- funny
- water
- o
- month
- dog
- fifty
- paper
- gotten
- found
- taking
- today
- certainly
- boy
- friends
- number
- mine
- program
- food
- son
- p
- older
- name
- air
- movies
- government
- moved
- schools
- outside
- deal
- close
- tried
- paying
- eat
- drive
- hours
- nine
- rather
- cars
- crime
- important
- war
- living
- between
- business
- anymore
- reason
- weeks
- public
- vote
- situation
- recently
- nothing
- easy
- sit
- pick
- taxes
- turn
- full
- percent
- making
- friend
- book
- happen
- minutes
- middle
- town
- watching
- paid
- eighty
- tax
- several
- listen
- set
- talked
- north
- takes
- reading
- definitely
- law
- jury
- kinds
- married
- u
- enjoyed
- says
- without
- works
- learn
- everyone
- drug
- major
- side
- cost
- room
- education
- morning
- computer
- involved
- mostly
- aren
- health
- l
- anybody
- along
- amount
- man
- against
- weather
- often
- under
- age
- forty
- insurance
- favorite
- hope
- card
- must
- happened
- lives
- left
- drugs
- expensive
- american
- miles
- yourself
- hour
- already
- plano
- cards
- decided
- large
- difference
- ahead
- fifteen
- camping
- told
- although
- second
- r
- woman
- twelve
- knew
- guys
- cut
- neat
- fish
- mind
- wrong
- unless
- sense
- instead
- leave
- wear
- class
- hand
- top
- walk
- bring
- past
- f
- running
- e
- absolutely
- weekend
- line
- books
- question
- team
- wish
- exercise
- interested
- areas
- baby
- states
- liked
- somewhere
- father
- experience
- phone
- case
- men
- lots
- cat
- society
- taken
- changed
- game
- worth
- seventy
- gun
- h
- wonder
- hit
- group
- service
- kept
- shows
- gosh
- early
- interest
- trouble
- control
- themselves
- ha
- finally
- using
- god
- dad
- cook
- hot
- difficult
- nursing
- front
- terms
- growing
- late
- kid
- looked
- felt
- rain
- teach
- tend
- realize
- weren
- sixty
- except
- needs
- social
- budget
- figure
- recycling
- lake
- wanna
- looks
- wh
- forth
- mom
- concerned
- south
- grew
- topic
- ways
- death
- christmas
- regular
- wait
- imagine
- television
- east
- trees
- check
- fairly
- hate
- general
- catch
- dinner
- built
- ready
- fine
- sister
- story
- playing
- starting
- homes
- office
- awful
- radio
- needed
- companies
- changes
- programs
- fishing
- nineteen
- ask
- tough
- cans
- easier
- yard
- cold
- ought
- street
- later
- door
- wants
- students
- national
- space
- across
- brother
- free
- local
- tha
- level
- happens
- sitting
- newspaper
- move
- countries
- store
- subject
- girl
- beautiful
- turned
- soon
- income
- putting
- church
- university
- dress
- information
- lately
- degree
- york
- vacation
- pollution
- totally
- winter
- america
- ah
- ours
- cats
- spent
- happy
- played
- consider
- cases
- spring
- california
- longer
- teacher
- oil
- send
- lost
- sports
- garden
- teachers
- families
- particular
- buying
- amazing
- likes
- football
- united
- teaching
- hey
- benefits
- brought
- gave
- party
- worry
- throw
- testing
- given
- bunch
- near
- nobody
- community
- driving
- open
- personal
- sell
- force
- chance
- wow
- test
- baseball
- within
- biggest
- quality
- building
- example
- seeing
- power
- afford
- support
- caught
- inside
- plan
- seemed
- ninety
- younger
- learned
- generation
- charge
- punishment
- rest
- dogs
- become
- clean
- short
- privacy
- g
- calls
- plus
- particularly
- decide
- terrible
- twice
- fall
- extra
- period
- choice
- hold
- ended
- hadn
- main
- guilty
- depends
- save
- excellent
- price
- strange
- feeling
- size
- trial
- military
- boys
- per
- bet
- judge
- parts
- noticed
- anywhere
- fan
- head
- center
- glad
- clothes
- rate
- stop
- eleven
- white
- stand
- suppose
- guns
- grade
- watched
- bigger
- scary
- issue
- special
- dollar
- green
- its
- jobs
- means
- black
- worse
- knows
- plastic
- low
- spending
- picked
- golf
- gas
- single
- neighborhood
- necessarily
- alone
- cooking
- newspapers
- pull
- fast
- completely
- road
- student
- crimes
- houses
- paint
- medical
- learning
- fair
- restaurant
- miss
- lawn
- giving
- washington
- doctor
- word
- killed
- recycle
- light
- cash
- visit
- familiar
- grass
- itself
- season
- chicken
- rid
- president
- stayed
- normally
- whenever
- machine
- graduate
- eighteen
- capital
- shouldn
- virginia
- private
- field
- magazines
- kill
- market
- apartment
- anyone
- waiting
- asked
- classes
- break
- crazy
- helps
- aware
- sunday
- hm
- speak
- term
- sound
- property
- sad
- comfortable
- waste
- channel
- evening
- cover
- heavy
- carry
- everyday
- systems
- gives
- wa
- answer
- higher
- unfortunately
- minute
- future
- serious
- snow
- available
- smaller
- handle
- ground
- behind
- huge
- west
- plant
- allowed
- wind
- peace
- costs
- cause
- serve
- rent
- lucky
- gee
- build
- english
- telling
- lose
- individual
- gardening
- busy
- order
- raised
- basic
- basis
- rock
- training
- happening
- opinion
- heart
- follow
- mainly
- history
- walking
- ye
- average
- towards
- houston
- games
- travel
- decision
- environment
- respect
- list
- hopefully
- grow
- others
- sorry
- san
- taught
- weight
- bags
- hurt
- finding
- attention
- hasn
- computers
- raise
- aerobics
- quick
- shot
- personally
- bedroom
- similar
- loved
- sixties
- park
- helping
- feet
- industry
- write
- generally
- weird
- record
- benefit
- pool
- mail
- pennsylvania
- glass
- notice
- calling
- process
- land
- originally
- richardson
- cities
- afraid
- utah
- entire
- colorado
- ball
- boat
- grandmother
- possible
- folks
- helped
- strong
- keeping
- bill
- keeps
- thank
- camp
- third
- types
- eventually
- obviously
- yesterday
- apparently
- instance
- pet
- central
- club
- flowers
- trash
- trip
- classical
- europe
- changing
- perhaps
- self
- color
- foot
- video
- based
- station
- saturday
- french
- normal
- fire
- '''clock'
- issues
- starts
- piece
- hobby
- quit
- prison
- parent
- oldest
- bush
- coverage
- police
- forget
- girls
- occasionally
- bank
- shape
- beginning
- moving
- sent
- vietnam
- nights
- current
- salary
- himself
- stories
- mountains
- aluminum
- luck
- invasion
- tape
- florida
- bed
- laws
- research
- mess
- hoping
- players
- tired
- thirteen
- magazine
- expect
- sleep
- words
- language
- push
- position
- hobbies
- background
- plants
- inches
- easily
- stopped
- murder
- shoot
- maryland
- hardly
- bills
- attitude
- pro
- civil
- sometime
- human
- wanting
- goodness
- security
- doctors
- kitchen
- somehow
- penalty
- county
- eating
- simply
- die
- bike
- reunion
- project
- typical
- j
- however
- total
- mexico
- base
- economy
- restaurants
- responsibility
- jail
- lower
- died
- tested
- safe
- voting
- elderly
- sh
- listening
- sudden
- numbers
- career
- stick
- born
- wondering
- poor
- painting
- active
- professional
- supposedly
- li
- lady
- reasons
- cool
- sixteen
- yep
- excuse
- horrible
- political
- red
- science
- federal
- besides
- shop
- opportunity
- ride
- planning
- degrees
- writing
- mexican
- engineering
- surprised
- bother
- share
- graduated
- account
- financial
- hands
- activities
- seventies
- step
- thanks
- bag
- role
- england
- limit
- willing
- hospital
- view
- band
- teams
- tonight
- groups
- advantage
- heat
- department
- turns
- tree
- telephone
- became
- brand
- criminal
- blue
- dry
- warm
- weekends
- grown
- stores
- rights
- garbage
- junior
- everywhere
- prices
- metric
- ran
- equipment
- till
- cross
- considered
- track
- moment
- figured
- americans
- met
- worst
- ridiculous
- grocery
- yours
- neighbor
- piano
- sold
- cowboys
- selling
- savings
- grandchildren
- nowadays
- add
- plays
- conversation
- lunch
- straight
- sentence
- floor
- dead
- fourteen
- meet
- ideas
- foods
- israel
- fix
- ourselves
- swimming
- upset
- sign
- sewing
- wood
- recipe
- van
- upon
- standard
- box
- win
- wall
- offer
- products
- otherwise
- pounds
- stations
- ex
- staying
- drop
- body
- carolina
- sales
- meal
- ice
- basketball
- mixed
- careful
- possibly
- sick
- farm
- retired
- compared
- western
- hearing
- finished
- separate
- mentioned
- soviet
- truck
- river
- defense
- oklahoma
- harder
- k
- re
- stuck
- cable
- trade
- favor
- positive
- related
- smoke
- effect
- various
- bottom
- awhile
- kindergarten
- beat
- court
- beach
- baltimore
- choose
- allow
- brown
- hang
- known
- sorts
- bathroom
- scared
- popular
- extremely
- politics
- hair
- policy
- wha
- saint
- covered
- ca
- sisters
- boston
- lakes
- forever
- fight
- downtown
- visa
- sauce
- garage
- lines
- suit
- whereas
- speech
- direction
- animals
- corps
- fit
- majority
- chinese
- dark
- painted
- milk
- concern
- dump
- nature
- safety
- shoes
- star
- questions
- switch
- clear
- trips
- management
- beyond
- depending
- sing
- iraq
- pressure
- cute
- runs
- windows
- salad
- board
- chicago
- population
- legal
- super
- '''all'
- puts
- slow
- pets
- forward
- thousands
- style
- debt
- becoming
- mo
- pop
- violent
- italian
- earlier
- cheap
- weapons
- coast
- austin
- traveling
- passed
- x
- speaking
- points
- prefer
- threat
- further
- master
- table
- broken
- random
- row
- northern
- simple
- appreciate
- district
- train
- continue
- rangers
- pittsburgh
- truth
- value
- quickly
- raising
- pass
- tennis
- flower
- bass
- engine
- becomes
- variety
- jeans
- exciting
- organization
- spread
- sat
- incredible
- somewhat
- loan
- engineer
- doubt
- southern
- monday
- backyard
- forced
- papers
- express
- saving
- owned
- recent
- toward
- fortunate
- liberal
- shopping
- rough
- brothers
- worried
- meals
- scouts
- vacations
- hunting
- lawyers
- wisconsin
- bucks
- act
- voice
- helpful
- wide
- retirement
- cannot
- picture
- picking
- suspect
- spare
- held
- election
- study
- report
- begin
- antonio
- drove
- opposed
- league
- ju
- se
- solution
- closer
- character
- finish
- knowing
- million
- common
- services
- thinks
- player
- violence
- wrote
- highway
- reasonable
- afternoon
- series
- developed
- effort
- christian
- fantastic
- saved
- seventeen
- barbecue
- sun
- conditioning
- ohio
- babies
- arlington
- hole
- visited
- rural
- herself
- knowledge
- kn
- plans
- instruments
- above
- border
- bible
- losing
- china
- events
- leaving
- written
- taste
- friday
- schedule
- anytime
- showed
- aspect
- range
- earth
- rice
- broke
- tent
- excited
- roles
- situations
- rooms
- spot
- laid
- duty
- bottles
- russia
- fighting
- pound
- letter
- convenient
- thi
- storm
- original
- wild
- showing
- percentage
- required
- grandparents
- extent
- economic
- voted
- canada
- trust
- healthy
- dealing
- face
- hired
- discuss
- larger
- pleased
- eye
- constantly
- perfect
- stupid
- square
- mix
- meat
- semester
- necessary
- mandatory
- burning
- fly
- mothers
- aids
- checked
- bedrooms
- fresh
- advice
- tomatoes
- treat
- sale
- ford
- japanese
- burn
- correct
- limited
- sleeping
- actual
- ends
- female
- hundreds
- feelings
- impact
- leaves
- section
- lay
- provide
- planted
- factor
- fill
- rich
- deep
- someplace
- drives
- circumstances
- honda
- jersey
- smoking
- feels
- fifties
- access
- doors
- pattern
- names
- payment
- facilities
- automatic
- boxes
- hi
- pictures
- versus
- ability
- edge
- politicians
- amazed
- boss
- union
- neighbors
- distance
- prime
- article
- mistake
- grades
- bread
- bothers
- jeez
- rented
- fourth
- alcohol
- gulf
- catfish
- license
- shooting
- touch
- asking
- realized
- require
- natural
- expenses
- purchase
- energy
- talks
- colors
- smart
- considering
- lessons
- tremendous
- participate
- ages
- missed
- quiet
- cheaper
- cents
- payments
- iron
- frightening
- forgot
- cheese
- daughters
- lawyer
- creek
- dental
- seat
- humid
- belt
- michigan
- extended
- flat
- driver
- foreign
- stays
- adults
- songs
- due
- wet
- double
- stress
- desert
- drink
- material
- equal
- deterrent
- machines
- eastern
- boring
- apart
- vegetables
- recipes
- unusual
- responsible
- hire
- garland
- ho
- dangerous
- loans
- colleges
- served
- prisons
- recycled
- cousins
- gorgeous
- member
- values
- fell
- fund
- metal
- wolves
- technology
- form
- enjoyable
- entertainment
- successful
- juries
- brings
- likely
- convicted
- appeal
- minimum
- opposite
- sport
- complete
- smell
- gallon
- lord
- employees
- centers
- alive
- blow
- meant
- cutting
- relatives
- bus
- commit
- none
- jus
- holding
- sand
- swing
- courses
- ski
- breed
- heck
- casual
- blood
- admit
- join
- fi
- draw
- upper
- bell
- youngest
- traffic
- protect
- tends
- medicine
- strongly
- committed
- opinions
- brick
- sides
- congress
- gasoline
- regularly
- plenty
- collect
- williams
- tickets
- perspective
- damage
- present
- bowl
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- landslided
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- outward
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- irritable
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- shooter
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- paranoid
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- anonymously
- competency
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- unfeasible
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- riddle
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- homogeneous
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- puree
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- reconvicted
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- orchestral
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- stormy
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- straighten
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- exhausts
- hairy
- gettingest
- daught
- bertinelli
- dysfunctional
- childfaring
- miracles
- bette
- midler
- redbook
- previewing
- postage
- unauthorized
- mayors
- discredit
- ps
- productions
- chariots
- gladiator
- fluent
- batches
- subtitle
- subtitled
- gems
- supernatural
- accusing
- migh
- mondays
- thrust
- lifters
- drills
- rocking
- referee
- abrasive
- maintaining
- posed
- refusing
- coins
- conversions
- dormitory
- unused
- ramp
- hydraulic
- disposer
- escapement
- incorporating
- leonard
- nimoy
- trekkie
- luke
- spock
- mccoy
- admiral
- hobbled
- vulcans
- doohan
- scotty
- addams
- averaging
- decrease
- munich
- snows
- chattanooga
- lori
- coldness
- membered
- unemp
- fetus
- complications
- slobs
- equation
- nameless
- malformed
- sincere
- deliberations
- dismissed
- indicted
- revenge
- subsequent
- provoked
- provocation
- qualifies
- mitigating
- contender
- linguini
- hawaiian
- luau
- angie
- shellfish
- clam
- cheeses
- nachos
- resurrection
- lutheran
- scanned
- cooperating
- toss
- inmate
- interpretation
- blanks
- executioner
- bamorghini
- skyhawk
- dominican
- nantes
- castles
- vineyard
- consignment
- goodwill
- crushes
- sewer
- res
- unoccupied
- assassinated
- menace
- perspec
- relativity
- vantage
- weighted
- reflect
- subservient
- integration
- ith
- frien
- drudgery
- montpe
- mont
- monteplier
- montpelier
- everett
- yack
- tromping
- unlimited
- wedge
- fairway
- flus
- startling
- '286'
- turret
- scien
- simulators
- plugged
- upgrades
- custer
- '386'
- trenches
- trencher
- stunt
- cul
- sac
- rearranged
- clancy
- novell
- netware
- ark
- ladonna
- peck
- bourne
- ultimatum
- enveloped
- amsterdam
- holland
- harpsichordist
- forte
- warrington
- cheating
- harry
- heroic
- mayfield
- corrupts
- lig
- hatteras
- imaging
- legalese
- himsnelf
- koop
- scarcity
- highland
- jogs
- gyms
- inequities
- stimulate
- deductor
- bentsen
- drunks
- lafferty
- infringe
- snuffed
- snuff
- compares
- gilmore
- accomplishes
- william
- thrice
- mating
- sows
- suckling
- hernia
- carcass
- cloves
- pineapples
- cranberries
- hominy
- barb
- automatics
- avis
- crashed
- lens
- porsche
- turbo
- carrera
- mys
- mushrooming
- percentagewise
- folderol
- lifeguard
- jarring
- flui
- watchers
- pokes
- blamed
- ceases
- intravenous
- cell
- quests
- subsidies
- slashed
- entitlement
- trades
- beauticians
- unending
- spiral
- consumers
- unf
- ailments
- magerick
- celtic
- transplanted
- rolando
- harper
- plaint
- straighter
- dayer
- plumbed
- bolted
- logan
- accredited
- professorship
- distressing
- fiel
- treasury
- refunds
- halt
- spying
- scaled
- loading
- challenger
- stat
- mirv
- roomy
- cargo
- recommends
- volvos
- wagons
- conscientiously
- emiss
- hypothesize
- muncie
- terre
- haute
- triggering
- verify
- drivable
- emerges
- overgrazed
- reclaimed
- prettiest
- palm
- paintbrush
- septic
- hummingbirds
- hummingbird
- pooped
- annuals
- countrified
- supermarket
- coaster
- afterburners
- gliding
- oomph
- subs
- gambled
- insulating
- spec
- verandas
- genes
- drapes
- guppies
- platies
- fishies
- glacier
- playgrounds
- wilderness
- scaries
- rayburn
- curling
- nominal
- fulfill
- synagogue
- geriatrics
- app
- degenerative
- communiky
- enhance
- assist
- text
- biogra
- daniels
- prince
- phillip
- criticizing
- miniseries
- scarlett
- spectacular
- torrents
- ligh
- horizontally
- arid
- crisp
- sleigh
- brighton
- springtime
- skie
- hammered
- subtly
- brianna
- lib
- submerged
- loosening
- leaks
- tar
- gravel
- plastered
- drywalled
- plastering
- terri
- exasperating
- swelling
- squirming
- swells
- shrinks
- retains
- highlight
- captive
- legos
- technic
- lego
- stare
- engagements
- sousa
- refreshments
- rehearsal
- donations
- municipal
- conduct
- nitny
- altoona
- lockhaven
- nighttimes
- ama
- emerson
- maceboast
- circuitry
- vacationer
- wausau
- unduly
- sunglasses
- grip
- durable
- faulty
- recliner
- pinto
- sequoias
- redwoods
- bryce
- tetons
- sequoia
- driveways
- snowmen
- snowballs
- marketed
- acceleration
- suspension
- lumbar
- sma
- bur
- skyrocketing
- govern
- exclude
- ballgame
- warrant
- rounds
- brats
- eff
- nativity
- facings
- casings
- relieve
- strase
- reliever
- relieving
- sander
- cabinet
- equipments
- dado
- rotary
- sicknesses
- bryan
- mamas
- packards
- solburns
- frown
- niggardly
- chintzy
- megs
- mirroring
- epidemic
- immunizations
- rays
- mumps
- rubella
- inaccuracy
- defined
- issued
- hypocritical
- stings
- laundering
- contr
- governed
- discomfort
- stea
- holster
- spontaneous
- headquarters
- bitterest
- fluctuations
- texts
- doen
- rosie
- '''neil'
- thomases
- trimmer
- clump
- tithing
- homeowner
- computerization
- stale
- subroutine
- libra
- clara
- beastie
- triggered
- pledged
- fren
- ally
- organi
- trombone
- weathers
- facetious
- directors
- spells
- compulsive
- childr
- fluffs
- toppings
- brea
- torque
- underdrive
- sportier
- beetle
- coolers
- bonneville
- secondaries
- quadrajet
- compulsion
- elevation
- variations
- hilltops
- mines
- hamster
- cruelty
- parakeet
- parakreet
- burmese
- deactivated
- infatuated
- jobbies
- visualize
- boggling
- slid
- clamped
- kisses
- everywh
- brag
- gramm
- overturning
- renegotiate
- kickbacks
- valdez
- defi
- batted
- hangs
- threats
- emit
- che
- churning
- remembrance
- networking
- conformance
- wyatt
- extremey
- bennigan
- vincent
- chefalia
- whataburger
- zillion
- mercado
- juarez
- tallest
- ewaldes
- cont
- stoneleigh
- chews
- yapping
- collies
- roughest
- hollered
- battling
- obedience
- squats
- vaca
- pilgrims
- medieval
- relics
- bemerton
- newness
- turin
- muffins
- requests
- helman
- tart
- zing
- cele
- layering
- fluffier
- joins
- jennifer
- unselfish
- tutoring
- affiliated
- aimlessly
- perky
- shins
- hyper
- burdensome
- earphones
- timbuktu
- onna
- lieutenant
- biologist
- sliding
- tremors
- variedly
- bakers
- aprons
- sweatshirt
- wigs
- lamb
- bunnies
- symbols
- milky
- polytechnochloride
- mought
- trashmore
- lifts
- riverview
- tranged
- strongest
- recessionary
- stagnate
- unteachable
- prominent
- chide
- remaining
- backbone
- newborns
- fullest
- firewh
- daffodil
- jung
- aquinas
- libretto
- rossini
- mahler
- dutchen
- trumpets
- elixir
- floated
- swapped
- tyme
- tempco
- trooper
- gisland
- carribean
- unpacking
- lotto
- alcatraz
- hairdresser
- crui
- janice
- furry
- eaves
- rafter
- cactuses
- furrows
- wrung
- plink
- construe
- thinkings
- bue
- buechele
- grieves
- gullible
- manufactures
- borden
- bib
- overalls
- oshman
- evaluated
- unfor
- linguistic
- austria
- niagara
- coasts
- carolinas
- leisurely
- modesto
- cheeseburgers
- incapable
- hygienic
- inoperable
- oxygen
- banish
- relocated
- realtor
- listings
- precautions
- integrate
- cooperatives
- reallocate
- reorganize
- accelerate
- transient
- commish
- tenderhearted
- galaxies
- crud
- mutations
- feazure
- ballooned
- reclamation
- merits
- axiom
- fiends
- sensitivity
- aboveboard
- evaluating
- veggies
- unarmed
- resembling
- tallow
- scalloped
- weighing
- strap
- squeaker
- closing
- mullin
- squeakers
- marquee
- bluish
- hydrogen
- sulfide
- h2s
- ramps
- vaccine
- preventable
- syringes
- needles
- feared
- ruf
- riffraff
- haves
- nots
- earhout
- bulletproof
- vest
- hedge
- tollbooth
- hatcher
- taverns
- sailboats
- ancle
- lounge
- cocktail
- sailer
- cruiser
- hull
- spars
- rigging
- gusts
- wearisome
- flaky
- markups
- arming
- stra
- quail
- swedish
- munch
- intermission
- doughy
- frosts
- iceberg
- schoolteacher
- altrusa
- upholstery
- garl
- jupiter
- musically
- auditions
- repertory
- outlet
- auditory
- lear
- educationally
- verified
- chording
- pianist
- min
- ec
- subbranch
- emigrated
- beware
- entrepreneurial
- ventures
- banked
- stored
- footsteps
- postcards
- notify
- notifying
- steals
- hides
- subsequently
- corrective
- leers
- downright
- outright
- shu
- newest
- apathetic
- absol
- prolong
- roofing
- retool
- zigzag
- kan
- untalented
- washed
- salvageable
- gluing
- feds
- interrupting
- faults
- caucasian
- educ
- thei
- officed
- deputy
- pruned
- gladiolas
- amaryllis
- conf
- plantings
- sprout
- narcissus
- psychic
- rerun
- activate
- rusted
- rusts
- fenders
- repainted
- acco
- dreary
- expen
- salting
- weinstocks
- wad
- hilt
- dolphene
- feelt
- throwed
- wheelchairs
- emjoy
- anheimer
- tela
- kindly
- innovated
- endeavors
- adam
- particulars
- abusive
- evolutionary
- duplication
- imagers
- allocate
- optimally
- squawk
- evolution
- insurers
- entity
- burnable
- ticketed
- charities
- braved
- suede
- cardigan
- appointments
- unlined
- toasty
- lightweight
- fireplaces
- dense
- ethanol
- smokestacks
- mowers
- wedded
- organism
- nutritionally
- bamba
- szechuan
- pancho
- binders
- assignments
- developments
- cashew
- avoiding
- suey
- disburse
- squeeze
- sq
- faculties
- pauper
- brokerage
- anticipation
- cherished
- commodity
- famuel
- slopes
- biness
- furlough
- promoted
- nec
- shasta
- salmon
- sk
- walleye
- fighters
- fillet
- foil
- seekers
- scrutiny
- tarrant
- bobsy
- accu
- smiled
- growled
- mistrials
- railroaded
- convalescent
- unsettling
- senile
- graying
- exercisings
- unaffordable
- restricts
- casse
- gabrielli
- bankrupted
- cello
- viola
- composers
- boutiques
- darling
- chanting
- canseco
- ramming
- vinny
- utility
- outweighing
- sundance
- smithsonian
- crosswords
- planners
- artists
- bazo
- faron
- spiro
- gyro
- dulcimer
- jarreau
- contorted
- bonnie
- rait
- grammy
- unedu
- sprayer
- routers
- cookie
- varnish
- smoother
- hayloft
- franklin
- gradual
- increasement
- torpedoed
- downside
- blythe
- tonkin
- macintoshes
- graphical
- multitasking
- gestures
- vocabulary
- compilers
- consultation
- interactive
- discriminating
- correlate
- funnest
- gentler
- panicked
- sassy
- westmin
- westminster
- infra
- mondale
- situa
- circuses
- disrepair
- dashboard
- ce
- beefing
- patrols
- visibility
- lifted
- cumberland
- cobb
- thefts
- superficial
- cracked
- electrically
- manufactured
- bordering
- elects
- aerodyne
- aerob
- brace
- publicize
- killings
- duri
- commentators
- blurbs
- bog
- dur
- countdown
- newscasts
- unreasonable
- moderator
- unorganized
- moderated
- assumingly
- importers
- dahlmer
- ohi
- nightmarish
- withheld
- sovereign
- martial
- puritanical
- permissible
- acquitting
- acquit
- impaneling
- dismissing
- foreman
- deliberating
- una
- restate
- unannounced
- sweep
- definitive
- bodily
- behaviors
- enters
- privacies
- melanie
- spry
- announcements
- anson
- fayetteville
- waynesboro
- delinquency
- fre
- gainfully
- tremen
- thriving
- towar
- grit
- pail
- latent
- compression
- ovens
- armor
- fierce
- finagle
- nationalizing
- cutoff
- operat
- unionized
- distinction
- institutionally
- expedient
- innovativeness
- expedi
- unequal
- plaintiff
- novices
- bets
- leaky
- luby
- taping
- promo
- blurb
- mutt
- hooper
- veterin
- spay
- neuter
- frie
- shorties
- decreased
- unrestricted
- glut
- magnum
- rushes
- oper
- preset
- styro
- frank
- shocks
- allot
- frowned
- chronicle
- analytical
- abnormality
- overwhelmingly
- academia
- descriptions
- addictive
- reevaluate
- divvy
- allocated
- psy
- psychedelic
- crosby
- stills
- performers
- secular
- druggie
- shipping
- maximize
- actuall
- revelation
- polymers
- roadways
- hoop
- funn
- heavenly
- retailers
- induce
- inducement
- recycler
- saskatoon
- welfor
- employing
- deposits
- arithmetic
- sums
- colleague
- internet
- infusions
- incurring
- surveying
- assesses
- footloose
- smattering
- greetings
- snobby
- paled
- refrained
- acute
- indivigal
- thrives
- categorized
- receptionist
- lar
- curve
- critter
- incumbent
- entrenched
- standardizing
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
use_preprocessor: true
token_type: word
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: s3prl
frontend_conf:
frontend_conf:
upstream: wav2vec2_large_ll60k
download_dir: ./hub
multilayer_feature: true
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
preencoder: linear
preencoder_conf:
input_size: 1024
output_size: 80
encoder: conformer
encoder_conf:
output_size: 512
attention_heads: 8
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 8
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.7a1
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
azaninello/distilgpt2-finetuned-shroomstoy | azaninello | 2022-03-04T19:13:30Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-04T19:07:36Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-shroomstoy
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. -->
# distilgpt2-finetuned-shroomstoy
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0958
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 10 | 4.1207 |
| No log | 2.0 | 20 | 4.1009 |
| No log | 3.0 | 30 | 4.0958 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
Kevincp560/distilbart-cnn-6-6-finetuned-pubmed | Kevincp560 | 2022-03-04T17:56:48Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:pub_med_summarization_dataset",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-04T12:49:07Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pub_med_summarization_dataset
metrics:
- rouge
model-index:
- name: distilbart-cnn-6-6-finetuned-pubmed
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: pub_med_summarization_dataset
type: pub_med_summarization_dataset
args: document
metrics:
- name: Rouge1
type: rouge
value: 39.2769
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbart-cnn-6-6-finetuned-pubmed
This model is a fine-tuned version of [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6) on the pub_med_summarization_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0648
- Rouge1: 39.2769
- Rouge2: 15.876
- Rougel: 24.2306
- Rougelsum: 35.267
- Gen Len: 141.8565
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 2.2215 | 1.0 | 4000 | 2.0781 | 37.2476 | 14.2852 | 22.6875 | 33.1607 | 141.97 |
| 2.0105 | 2.0 | 8000 | 2.0217 | 37.8038 | 14.7869 | 23.2025 | 33.7069 | 141.918 |
| 1.8331 | 3.0 | 12000 | 2.0243 | 39.0497 | 15.8077 | 24.2237 | 34.9371 | 141.822 |
| 1.6936 | 4.0 | 16000 | 2.0487 | 38.7059 | 15.4364 | 23.8514 | 34.7771 | 141.878 |
| 1.5817 | 5.0 | 20000 | 2.0648 | 39.2769 | 15.876 | 24.2306 | 35.267 | 141.8565 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
daisyxie21/bert-base-uncased-8-10-0.01 | daisyxie21 | 2022-03-04T16:27:40Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-04T14:27:09Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-8-10-0.01
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-8-10-0.01
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8324
- Matthews Correlation: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.01
- 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 | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| No log | 1.0 | 400 | 0.8324 | 0.0 |
| 1.0904 | 2.0 | 800 | 1.3157 | 0.0 |
| 0.9461 | 3.0 | 1200 | 0.4407 | 0.0 |
| 0.9565 | 4.0 | 1600 | 2.1082 | 0.0 |
| 1.024 | 5.0 | 2000 | 0.7220 | 0.0 |
| 1.024 | 6.0 | 2400 | 0.7414 | 0.0 |
| 0.8362 | 7.0 | 2800 | 0.4442 | 0.0 |
| 0.6765 | 8.0 | 3200 | 0.5481 | 0.0 |
| 0.5902 | 9.0 | 3600 | 0.5642 | 0.0 |
| 0.5476 | 10.0 | 4000 | 0.4449 | 0.0 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
dragonSwing/wav2vec2-base-vn-270h | dragonSwing | 2022-03-04T15:05:51Z | 81 | 8 | speechbrain | [
"speechbrain",
"wav2vec2",
"audio",
"speech",
"Transformer",
"automatic-speech-recognition",
"vi",
"dataset:vivos",
"dataset:common_voice",
"license:cc-by-nc-4.0",
"model-index",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: vi
datasets:
- vivos
- common_voice
metrics:
- wer
pipeline_tag: automatic-speech-recognition
tags:
- audio
- speech
- speechbrain
- Transformer
license: cc-by-nc-4.0
widget:
- example_title: Example 1
src: https://huggingface.co/dragonSwing/wav2vec2-base-vn-270h/raw/main/example.mp3
- example_title: Example 2
src: https://huggingface.co/dragonSwing/wav2vec2-base-vn-270h/raw/main/example2.mp3
model-index:
- name: Wav2vec2 Base Vietnamese 270h
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice vi
type: common_voice
args: vi
metrics:
- name: Test WER
type: wer
value: 9.66
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: vi
metrics:
- name: Test WER
type: wer
value: 5.57
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: vi
metrics:
- name: Test WER
type: wer
value: 5.76
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: VIVOS
type: vivos
args: vi
metrics:
- name: Test WER
type: wer
value: 3.70
---
# Wav2Vec2-Base-Vietnamese-270h
Fine-tuned Wav2Vec2 model on Vietnamese Speech Recognition task using about 270h labelled data combined from multiple datasets including [Common Voice](https://huggingface.co/datasets/common_voice), [VIVOS](https://huggingface.co/datasets/vivos), [VLSP2020](https://vlsp.org.vn/vlsp2020/eval/asr). The model was fine-tuned using SpeechBrain toolkit with a custom tokenizer. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io/).
When using this model, make sure that your speech input is sampled at 16kHz.
Please refer to [huggingface blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) or [speechbrain](https://github.com/speechbrain/speechbrain/tree/develop/recipes/CommonVoice/ASR/CTC) on how to fine-tune Wav2Vec2 model on a specific language.
### Benchmark WER result:
| | [VIVOS](https://huggingface.co/datasets/vivos) | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) |
|---|---|---|---|
|without LM| 8.23 | 12.15 | 12.15 |
|with 4-grams LM| 3.70 | 5.57 | 5.76 |
The language model was trained using [OSCAR](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) dataset on about 32GB of crawled text.
### Install SpeechBrain
To use this model, you should install speechbrain > 0.5.10
### Usage
The model can be used directly (without a language model) as follows:
```python
from speechbrain.pretrained import EncoderASR
model = EncoderASR.from_hparams(source="dragonSwing/wav2vec2-base-vn-270h", savedir="pretrained_models/asr-wav2vec2-vi")
model.transcribe_file('dragonSwing/wav2vec2-base-vn-270h/example.mp3')
# Output: được hồ chí minh coi là một động lực lớn của sự phát triển đất nước
```
### Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
### Evaluation
The model can be evaluated as follows on the Vietnamese test data of Common Voice 8.0.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric, Audio
from transformers import Wav2Vec2FeatureExtractor
from speechbrain.pretrained import EncoderASR
import re
test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "vi", split="test", use_auth_token=True)
test_dataset = test_dataset.cast_column("audio", Audio(sampling_rate=16_000))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
wer = load_metric("wer")
extractor = Wav2Vec2FeatureExtractor.from_pretrained("dragonSwing/wav2vec2-base-vn-270h")
model = EncoderASR.from_hparams(source="dragonSwing/wav2vec2-base-vn-270h", savedir="pretrained_models/asr-wav2vec2-vi", run_opts={'device': device})
chars_to_ignore_regex = r'[,?.!\-;:"“%\'�]'
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
audio = batch["audio"]
batch["target_text"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
batch['speech'] = audio['array']
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
def evaluate(batch):
# For padding inputs only
inputs = extractor(
batch['speech'],
sampling_rate=16000,
return_tensors="pt",
padding=True,
do_normalize=False
).input_values
input_lens = torch.ones(inputs.shape[0])
pred_str, pred_tokens = model.transcribe_batch(inputs, input_lens)
batch["pred_strings"] = pred_str
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=1)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["target_text"])))
```
**Test Result**: 12.155553%
#### Citation
```
@misc{SB2021,
author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
title = {SpeechBrain},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
}
```
#### About SpeechBrain
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.
Website: [https://speechbrain.github.io](https://speechbrain.github.io/)
GitHub: [https://github.com/speechbrain/speechbrain](https://github.com/speechbrain/speechbrain) |
Ayham/bert_ernie_summarization_cnn_dailymail | Ayham | 2022-03-04T12:51:38Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-04T05:25:42Z | ---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: bert_ernie_summarization_cnn_dailymail
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_ernie_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
NbAiLab/roberta_jan_512_ncc | NbAiLab | 2022-03-04T11:44:03Z | 60 | 0 | transformers | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | ---
license: cc-by-sa-4.0
---
|
gustavecortal/T0_3B-8bit | gustavecortal | 2022-03-04T10:32:31Z | 6 | 10 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"fr",
"dataset:bigscience/P3",
"arxiv:2110.08207",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: fr
license: mit
tags:
- en
datasets:
- bigscience/P3
---
### Quantized BigScience's T0 3B with 8-bit weights
This is a version of [BigScience's T0](https://huggingface.co/bigscience/T0_3B) with 3 billion parameters that is modified so you can generate **and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti)**. Inspired by [GPT-J 8bit](https://huggingface.co/hivemind/gpt-j-6B-8bit).
Here's how to run it: [](https://colab.research.google.com/drive/1lMja-CPc0vm5_-gXNXAWU-9c0nom7vZ9)
This model can be easily loaded using the `T5ForConditionalGeneration` functionality:
```python
from transformers import T5ForConditionalGeneration
model = T5ForConditionalGeneration.from_pretrained("gustavecortal/T0_3B-8bit")
```
Before loading, you have to Monkey-Patch T5:
```python
class T5ForConditionalGeneration(transformers.models.t5.modeling_t5.T5ForConditionalGeneration):
def __init__(self, config):
super().__init__(config)
convert_to_int8(self)
transformers.models.t5.modeling_t5.T5ForConditionalGeneration = T5ForConditionalGeneration
```
## Model Description
T0* shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller. It is a series of encoder-decoder models trained on a large set of different tasks specified in natural language prompts. We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. To obtain T0*, we fine-tune a pretrained language model on this multitask mixture covering many different NLP tasks.
## Links
* [BigScience](https://bigscience.huggingface.co/)
* [Hivemind](https://training-transformers-together.github.io/)
* [Gustave Cortal](https://twitter.com/gustavecortal)
```bibtex
@misc{sanh2021multitask,
title={Multitask Prompted Training Enables Zero-Shot Task Generalization},
author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Stella Biderman and Leo Gao and Tali Bers and Thomas Wolf and Alexander M. Rush},
year={2021},
eprint={2110.08207},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
``` |
public-data/yu4u-age-estimation-pytorch | public-data | 2022-03-04T09:40:41Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-04T09:37:49Z | # yu4u/age-estimation-pytorch
- Repo: https://github.com/yu4u/age-estimation-pytorch
- Model: https://github.com/yu4u/age-estimation-pytorch/releases/download/v1.0/epoch044_0.02343_3.9984.pth
|
Yulinfeng/wsj0_2mix_enh_train_enh_dpcl_raw_valid.si_snr.ave | Yulinfeng | 2022-03-04T07:01:30Z | 0 | 0 | espnet | [
"espnet",
"audio",
"audio-to-audio",
"en",
"dataset:wsj0_2mix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | audio-to-audio | 2022-03-04T07:00:57Z | ---
tags:
- espnet
- audio
- audio-to-audio
language: en
datasets:
- wsj0_2mix
license: cc-by-4.0
---
## ESPnet2 ENH model
### `Yulinfeng/wsj0_2mix_enh_train_enh_dpcl_raw_valid.si_snr.ave`
This model was trained by earthmanylf using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout ec1acec03d109f06d829b80862e0388f7234d0d1
pip install -e .
cd egs2/wsj0_2mix/enh1
./run.sh --skip_data_prep false --skip_train true --download_model Yulinfeng/wsj0_2mix_enh_train_enh_dpcl_raw_valid.si_snr.ave
```
<!-- Generated by ./scripts/utils/show_enh_score.sh -->
# RESULTS
## Environments
- date: `Thu Feb 24 16:26:21 CST 2022`
- python version: `3.8.10 (default, May 19 2021, 18:05:58) [GCC 7.3.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.5.1+cu101`
- Git hash: `c58adabbe1b83dcd0b616ecd336b4a0648334e2c`
- Commit date: `Wed Feb 16 14:20:38 2022 +0800`
## ..
config: conf/tuning/train_enh_dpcl.yaml
|dataset|PESQ|STOI|SAR|SDR|SIR|SI_SNR|
|---|---|---|---|---|---|---|
|enhanced_cv_min_8k|2.18|0.84|9.63|8.59|17.31|8.04|
|enhanced_tt_min_8k|2.15|0.84|9.51|8.45|17.22|7.91|
## ENH config
<details><summary>expand</summary>
```
config: conf/tuning/train_enh_dpcl.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/enh_train_enh_dpcl_raw
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 2
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 43204
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: 10
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- si_snr
- max
- - valid
- loss
- min
keep_nbest_models: 1
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 8
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/enh_stats_8k/train/speech_mix_shape
- exp/enh_stats_8k/train/speech_ref1_shape
- exp/enh_stats_8k/train/speech_ref2_shape
valid_shape_file:
- exp/enh_stats_8k/valid/speech_mix_shape
- exp/enh_stats_8k/valid/speech_ref1_shape
- exp/enh_stats_8k/valid/speech_ref2_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 80000
- 80000
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/tr_min_8k/wav.scp
- speech_mix
- sound
- - dump/raw/tr_min_8k/spk1.scp
- speech_ref1
- sound
- - dump/raw/tr_min_8k/spk2.scp
- speech_ref2
- sound
valid_data_path_and_name_and_type:
- - dump/raw/cv_min_8k/wav.scp
- speech_mix
- sound
- - dump/raw/cv_min_8k/spk1.scp
- speech_ref1
- sound
- - dump/raw/cv_min_8k/spk2.scp
- speech_ref2
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.001
eps: 1.0e-08
weight_decay: 1.0e-07
scheduler: reducelronplateau
scheduler_conf:
mode: min
factor: 0.7
patience: 1
init: xavier_uniform
model_conf:
stft_consistency: false
loss_type: mask_mse
mask_type: PSM
ref_channel: 0
criterions:
- name: dpcl
conf:
loss_type: dpcl
wrapper: dpcl
wrapper_conf:
weight: 1.0
use_preprocessor: false
encoder: stft
encoder_conf:
n_fft: 256
hop_length: 128
separator: dpcl
separator_conf:
rnn_type: blstm
num_spk: 2
nonlinear: relu
layer: 2
unit: 500
dropout: 0.1
emb_D: 40
decoder: stft
decoder_conf:
n_fft: 256
hop_length: 128
required:
- output_dir
version: 0.10.7a1
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{ESPnet-SE,
author = {Chenda Li and Jing Shi and Wangyou Zhang and Aswin Shanmugam Subramanian and Xuankai Chang and
Naoyuki Kamo and Moto Hira and Tomoki Hayashi and Christoph B{"{o}}ddeker and Zhuo Chen and Shinji Watanabe},
title = {ESPnet-SE: End-To-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration},
booktitle = {{IEEE} Spoken Language Technology Workshop, {SLT} 2021, Shenzhen, China, January 19-22, 2021},
pages = {785--792},
publisher = {{IEEE}},
year = {2021},
url = {https://doi.org/10.1109/SLT48900.2021.9383615},
doi = {10.1109/SLT48900.2021.9383615},
timestamp = {Mon, 12 Apr 2021 17:08:59 +0200},
biburl = {https://dblp.org/rec/conf/slt/Li0ZSCKHHBC021.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
abdelhalim/Shower_Sound_Recognition | abdelhalim | 2022-03-03T22:09:48Z | 20 | 3 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"audio",
"audio-classificaiton",
"shower detection",
"dataset:SHD-2",
"endpoints_compatible",
"region:us"
] | audio-classification | 2022-03-02T23:29:05Z | ---
datasets:
- SHD-2
tags:
- audio
- audio-classificaiton
- shower detection
metrics:
- Accuracy
---
**Context**
Most of our great brilliant ideas happen in periods of relaxation, like taking a
shower, however, once we leave the shower, we forget the brilliant idea. What if
we do not forget, and collect your ideas in the shower?
**What is the Shower Ideas concept?**
This is an app that detects when someone is taking a shower (douche) and asks
“do you have any idea?”, and the person will speak while taking the shower telling
the idea. And also will ask questions after taking a shower.
**Abstract about the model**
This model was trained based on *facebook/wav2vec2-base-960h* (which is a pretrained model on 960 hours of Librispeech on 16kHz sampled speech audio.) in order to classify the audio input into shower or no_shower.
**Dataset**
The SHD-2 dataset is a labeled collection of 2260 audio recordings of shower and no shower sounds.
The dataset consists of 6-second-long recordings organized into 2 classes (with 1130 examples per class).
# Usage
In order to use the model in your Python script just copy the following code:
```python
from transformers import pipeline
audio_input = 'example.wav'
classifier = pipeline("audio-classification", model="abdelhalim/Shower_Sound_Recognition")
labels = classifier(audio_input)
labels
``` |
batterydata/batteryscibert-uncased-squad-v1 | batterydata | 2022-03-03T20:28:37Z | 13 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"question answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
language: en
tags: question answering
license: apache-2.0
datasets:
- squad
- batterydata/battery-device-data-qa
metrics: squad
---
# BatterySciBERT-uncased for QA
**Language model:** batteryscibert-uncased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 32
n_epochs = 3
base_LM_model = "batteryscibert-uncased"
max_seq_len = 386
learning_rate = 2e-5
doc_stride=128
max_query_length=64
```
## Performance
Evaluated on the SQuAD v1.0 dev set.
```
"exact": 79.81,
"f1": 87.66,
```
Evaluated on the battery device dataset.
```
"precision": 66.65,
"recall": 85.29,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "batterydata/batteryscibert-uncased-squad-v1"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'What is the electrolyte?',
'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement |
batterydata/batteryonlybert-cased-squad-v1 | batterydata | 2022-03-03T20:25:04Z | 11 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"question answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
language: en
tags: question answering
license: apache-2.0
datasets:
- squad
- batterydata/battery-device-data-qa
metrics: squad
---
# BatteryOnlyBERT-cased for QA
**Language model:** batteryonlybert-cased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 16
n_epochs = 3
base_LM_model = "batteryonlybert-cased"
max_seq_len = 386
learning_rate = 2e-5
doc_stride=128
max_query_length=64
```
## Performance
Evaluated on the SQuAD v1.0 dev set.
```
"exact": 79.61,
"f1": 87.30,
```
Evaluated on the battery device dataset.
```
"precision": 64.28,
"recall": 82.72,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "batterydata/batteryonlybert-cased-squad-v1"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'What is the electrolyte?',
'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement |
batterydata/batteryonlybert-uncased-squad-v1 | batterydata | 2022-03-03T20:25:01Z | 16 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"question answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
language: en
tags: question answering
license: apache-2.0
datasets:
- squad
- batterydata/battery-device-data-qa
metrics: squad
---
# BatteryOnlyBERT-uncased for QA
**Language model:** batteryonlybert-uncased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 16
n_epochs = 2
base_LM_model = "batteryonlybert-uncased"
max_seq_len = 386
learning_rate = 2e-5
doc_stride=128
max_query_length=64
```
## Performance
Evaluated on the SQuAD v1.0 dev set.
```
"exact": 79.53,
"f1": 87.22,
```
Evaluated on the battery device dataset.
```
"precision": 67.20,
"recall": 83.82,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "batterydata/batteryonlybert-uncased-squad-v1"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'What is the electrolyte?',
'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement |
Kevincp560/wikihow-t5-small-finetuned-pubmed | Kevincp560 | 2022-03-03T20:22:04Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:pub_med_summarization_dataset",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-03T19:09:22Z | ---
tags:
- generated_from_trainer
datasets:
- pub_med_summarization_dataset
metrics:
- rouge
model-index:
- name: wikihow-t5-small-finetuned-pubmed
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: pub_med_summarization_dataset
type: pub_med_summarization_dataset
args: document
metrics:
- name: Rouge1
type: rouge
value: 8.9619
---
<!-- 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. -->
# wikihow-t5-small-finetuned-pubmed
This model is a fine-tuned version of [deep-learning-analytics/wikihow-t5-small](https://huggingface.co/deep-learning-analytics/wikihow-t5-small) on the pub_med_summarization_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2702
- Rouge1: 8.9619
- Rouge2: 3.2719
- Rougel: 8.1558
- Rougelsum: 8.5714
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.5984 | 1.0 | 4000 | 2.3696 | 10.237 | 3.8609 | 8.9776 | 9.677 | 19.0 |
| 2.5677 | 2.0 | 8000 | 2.3132 | 9.302 | 3.4499 | 8.3816 | 8.8831 | 19.0 |
| 2.5038 | 3.0 | 12000 | 2.2884 | 9.0578 | 3.3103 | 8.23 | 8.6723 | 19.0 |
| 2.4762 | 4.0 | 16000 | 2.2758 | 9.0001 | 3.2882 | 8.1845 | 8.6084 | 19.0 |
| 2.4393 | 5.0 | 20000 | 2.2702 | 8.9619 | 3.2719 | 8.1558 | 8.5714 | 19.0 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
repro-rights-amicus-briefs/legal-bert-base-uncased-finetuned-RRamicus | repro-rights-amicus-briefs | 2022-03-03T20:21:45Z | 11 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: legal-bert-base-uncased-finetuned-RRamicus
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. -->
# legal-bert-base-uncased-finetuned-RRamicus
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1520
## 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: 928
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.021 | 1.0 | 1118 | 1.3393 |
| 1.2272 | 2.0 | 2236 | 1.2612 |
| 1.2467 | 3.0 | 3354 | 1.2403 |
| 1.2149 | 4.0 | 4472 | 1.2276 |
| 1.1855 | 5.0 | 5590 | 1.2101 |
| 1.1674 | 6.0 | 6708 | 1.2020 |
| 1.1508 | 7.0 | 7826 | 1.1893 |
| 1.1386 | 8.0 | 8944 | 1.1870 |
| 1.129 | 9.0 | 10062 | 1.1794 |
| 1.1193 | 10.0 | 11180 | 1.1759 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
batterydata/bert-base-uncased-squad-v1 | batterydata | 2022-03-03T19:53:31Z | 69 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"question answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
language: en
tags: question answering
license: apache-2.0
datasets:
- squad
- batterydata/battery-device-data-qa
metrics: squad
---
# BERT-base-cased for QA
**Language model:** bert-base-uncased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 32
n_epochs = 3
base_LM_model = "bert-base-uncased"
max_seq_len = 386
learning_rate = 3e-5
doc_stride=128
max_query_length=64
```
## Performance
Evaluated on the SQuAD v1.0 dev set.
```
"exact": 80.93,
"f1": 88.20,
```
Evaluated on the battery device dataset.
```
"precision": 62.19,
"recall": 75.00,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "batterydata/bert-base-uncased-squad-v1"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'What is the electrolyte?',
'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement |
espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_wav2vec2 | espnet | 2022-03-03T15:32:39Z | 2 | 0 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:iemocap",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2022-03-03T15:29:59Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- iemocap
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_wav2vec2`
This model was trained by YushiUeda using iemocap recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout cf73065ba66cf6efb94af4415f0facaaef86abf6
pip install -e .
cd egs2/iemocap/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_wav2vec2
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Thu Mar 3 00:09:55 EST 2022`
- python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.9.0+cu102`
- Git hash: `cf73065ba66cf6efb94af4415f0facaaef86abf6`
- Commit date: `Sun Feb 27 19:56:48 2022 -0500`
## Using Conformer based encoder, Transformer based decoder, and self-supervised learning features with spectral augmentation and predicting transcript along with sentiment
- ASR config: [conf/tuning/train_asr_conformer_wav2vec2.yaml](conf/tuning/train_asr_conformer_wav2vec2.yaml)
- token_type: word
- Sentiment Labels: Positive, Neutral, Negative
|dataset|Snt|Intent Classification Macro F1 (%)| Weighted F1 (%)| Micro F1 (%)|
|---|---|---|---|---|
|decode_asr_model_valid.acc.ave_10best/valid|754|62.4|73.2|74.7|
|decode_asr_model_valid.acc.ave_10best/test|1650|61.1|64.8|66.1|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_conformer_wav2vec2.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer_wav2vec2_raw_en_word
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param:
- frontend.upstream
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_word/train/speech_shape
- exp/asr_stats_raw_en_word/train/text_shape.word
valid_shape_file:
- exp/asr_stats_raw_en_word/valid/speech_shape
- exp/asr_stats_raw_en_word/valid/text_shape.word
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train/wav.scp
- speech
- sound
- - dump/raw/train/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/valid/wav.scp
- speech
- sound
- - dump/raw/valid/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.0002
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
token_list:
- <blank>
- <unk>
- i
- you
- Negative
- to
- it
- '''s'
- the
- '''t'
- that
- and
- Neutral
- Positive
- a
- know
- what
- of
- like
- we
- don
- just
- is
- do
- this
- '''m'
- me
- have
- can
- in
- for
- 'no'
- so
- not
- '''re'
- my
- but
- mean
- be
- going
- all
- was
- they
- well
- want
- yeah
- right
- get
- 'on'
- there
- he
- oh
- here
- go
- out
- with
- your
- if
- okay
- are
- she
- at
- '''ll'
- '''ve'
- got
- think
- about
- up
- see
- then
- why
- how
- time
- really
- one
- now
- or
- as
- back
- look
- her
- him
- been
- because
- 'yes'
- would
- didn
- little
- did
- good
- some
- them
- something
- need
- maybe
- never
- um
- come
- take
- god
- had
- could
- will
- uh
- am
- people
- thing
- when
- very
- let
- much
- sorry
- from
- again
- long
- give
- anything
- too
- make
- fish
- years
- where
- isn
- three
- said
- things
- nothing
- help
- work
- tell
- guess
- over
- 'off'
- business
- even
- sir
- any
- his
- around
- were
- way
- who
- new
- kind
- '''d'
- our
- everything
- more
- came
- an
- should
- down
- understand
- only
- great
- else
- man
- line
- us
- ask
- last
- doing
- say
- waiting
- other
- lot
- job
- feel
- yourself
- point
- thought
- day
- whole
- away
- coming
- better
- marry
- always
- these
- still
- wrong
- two
- sure
- care
- phone
- probably
- remember
- annie
- life
- year
- believe
- gonna
- supposed
- went
- first
- talk
- listen
- alright
- before
- thinking
- after
- stuff
- happy
- ever
- turn
- thank
- home
- fine
- into
- than
- call
- money
- stay
- actually
- every
- hope
- love
- huh
- married
- wait
- somewhere
- has
- being
- father
- larry
- hell
- wanted
- trying
- getting
- guys
- name
- saying
- bag
- hear
- girl
- hey
- flashlight
- beach
- put
- leave
- dollars
- mind
- augie
- does
- won
- fifty
- excited
- hate
- four
- done
- through
- their
- keep
- car
- lost
- doesn
- happen
- wouldn
- school
- big
- calm
- night
- '''cause'
- id
- another
- though
- myself
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- pretend
- protection
- rid
- submit
- print
- regarding
- grievance
- sites
- protected
- processed
- careful
- secure
- unreliable
- trash
- kept
- spotting
- certain
- specifically
- pushing
- headed
- ears
- watched
- sends
- ceaseless
- wear
- often
- pleasure
- sonya
- promoted
- nurses
- mommy
- va
- videotaped
- cousin
- postpone
- performance
- swear
- cast
- spotlight
- microphone
- tripped
- surprise
- scored
- points
- members
- loser
- marrying
- weddings
- carats
- lousy
- chaperone
- drowsy
- deserve
- cry
- tears
- happiness
- marriage
- commercials
- refection
- financially
- studied
- passing
- russel
- crowe
- pooling
- funds
- owe
- learning
- role
- auditions
- denny
- tip
- teaching
- oof
- france
- steal
- keys
- laughing
- rosenkrantz
- thingy
- bopper
- limit
- whoa
- ways
- suffered
- disease
- handsome
- gifted
- parent
- ripped
- uveny
- tricia
- chemo
- baseball
- benny
- nat
- nation
- bread
- eat
- beer
- dorm
- sometime
- mattresses
- reserved
- grauman
- scale
- whooooo
- acti
- film
- art
- academy
- films
- fuck
- ethiopia
- cuddle
- profanity
- provider
- satellites
- average
- compensating
- unbeknownst
- satellite
- exaggerate
- advising
- addressed
- fax
- dumb
- fritz
- incoming
- million
- grown
- fella
- shootin
- travel
- sat
- instinct
- goosebumps
- arms
- danced
- intimately
- spart
- strumpets
- bristling
- diamonds
- taste
- portion
- side
- stairs
- condescending
- copy
- proceed
- remove
- missy
- behaving
- sweetie
- deploy
- specialist
- increase
- triple
- promotion
- retire
- quiets
- faster
- career
- lame
- drew
- barrymore
- nasty
- mouse
- cheesy
- jane
- tarzan
- engaged
- esmeralda
- hitched
- spontaneous
- character
- conga
- dim
- pulled
- chucky
- sarah
- guiding
- graduated
- apply
- colleges
- energy
- busing
- clerk
- excuses
- qualified
- chang
- investment
- banking
- deloitte
- touche
- temp
- degrading
- smarter
- astronaut
- biomedical
- internship
- plus
- breaking
- evicting
- typing
- shoot
- degree
- science
- club
- joking
- doomed
- maryland
- cooperate
- emergency
- pounds
- urn
- deduction
- sherlock
- holmes
- vessel
- burst
- caption
- therefore
- placed
- firing
- lobby
- fastest
- ibm
- misplace
- count
- hanging
- explanation
- follow
- footsteps
- overboard
- paralyzed
- coma
- fucked
- studying
- countries
- goal
- met
- greatest
- hopefully
- mmmm
- cinema
- chapter
- professionals
- sipping
- martinis
- sushi
- vat
- assistance
- starve
- south
- central
- firm
- police
- officer
- viacom
- digits
- speaking
- network
- charging
- connect
- outages
- hurricane
- katrina
- chose
- maam
- proven
- failing
- receive
- cuts
- using
- flip
- writing
- ms
- fall
- older
- game
- orange
- pink
- goodies
- battling
- sees
- flat
- stronger
- acted
- deserves
- hats
- shore
- pokes
- nah
- paul
- boats
- dammit
- enjoys
- bound
- harm
- pleasured
- lure
- devil
- rile
- topic
- initialed
- lets
- correctly
- spelled
- signed
- shitty
- timing
- susie
- tours
- emotionally
- bullshit
- enlist
- lie
- traditional
- church
- cabins
- flowery
- naturey
- midsummer
- excitement
- hoping
- attacked
- bears
- trim
- cooler
- dog
- tanish
- contrast
- cake
- buffet
- fried
- chicken
- mashed
- potatoes
- happier
- thrilled
- ecstatic
- rushed
- pressure
- interviews
- favors
- bite
- excessive
- unemployed
- cab
- gas
- possibly
- extreme
- trained
- presentable
- quote
- buck
- chugging
- engine
- realm
- minimum
- wage
- fry
- flipper
- bottom
- clear
- affect
- cle
- dressed
- shave
- legs
- presentation
- eighty
- success
- position
- training
- mcdonalds
- tv
- rainbow
- colored
- crap
- safely
- destination
- percoes
- equivalent
- amends
- courtesy
- inconveniencing
- near
- communicate
- conditions
- frequently
- current
- expecting
- pissed
- honor
- grandmother
- condition
- inevitable
- peace
- general
- mace
- present
- knife
- puny
- underwater
- basket
- weaving
- lying
- decided
- works
- worried
- occasion
- cruisers
- vibe
- greek
- lessons
- suck
- celebrating
- crush
- throughout
- test
- waters
- movies
- vermont
- cruiser
- abused
- frat
- boys
- dorms
- dell
- requests
- fixed
- dealt
- worries
- refunded
- situa
- relevant
- ordered
- orders
- others
- incorrectly
- tomatoes
- del
- cents
- attached
- cuz
- hoped
- opportunity
- rushing
- goods
- skipped
- breath
- kleenex
- alaska
- bearing
- hated
- holes
- calf
- witch
- whore
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
use_preprocessor: true
token_type: word
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: s3prl
frontend_conf:
frontend_conf:
upstream: wav2vec2_large_ll60k
download_dir: ./hub
multilayer_feature: true
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
preencoder: linear
preencoder_conf:
input_size: 1024
output_size: 80
encoder: conformer
encoder_conf:
output_size: 512
attention_heads: 8
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 8
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.7a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
sanchit-gandhi/wav2vec2-2-rnd-grid-search | sanchit-gandhi | 2022-03-03T14:51:05Z | 15 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
datasets:
- librispeech_asr
model-index:
- name: ''
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. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 6.9475
- Wer: 2.0097
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.9006 | 1.68 | 1500 | 6.9507 | 2.0097 |
| 6.9503 | 3.36 | 3000 | 6.9475 | 2.0097 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
|
keras-io/pointnet_segmentation | keras-io | 2022-03-03T12:43:46Z | 13 | 5 | tf-keras | [
"tf-keras",
"pointnet",
"segmentation",
"3d",
"image",
"arxiv:1612.00593",
"arxiv:1506.02025",
"license:cc0-1.0",
"region:us"
] | null | 2022-03-02T23:29:05Z | ---
tags:
- pointnet
- segmentation
- 3d
- image
license: cc0-1.0
---
## Point cloud segmentation with PointNet
This repo contains [an Implementation of a PointNet-based model for segmenting point clouds.](https://keras.io/examples/vision/pointnet_segmentation/).
Full credits to [Soumik Rakshit](https://github.com/soumik12345), [Sayak Paul](https://github.com/sayakpaul)
## Background Information
A "point cloud" is an important type of data structure for storing geometric shape data. Due to its irregular format, it's often transformed into regular 3D voxel grids or collections of images before being used in deep learning applications, a step which makes the data unnecessarily large. The PointNet family of models solves this problem by directly consuming point clouds, respecting the permutation-invariance property of the point data. The PointNet family of models provides a simple, unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing.
In this example, we demonstrate the implementation of the PointNet architecture for shape segmentation.
**References**
* [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://arxiv.org/abs/1612.00593)
* [Point cloud classification with PointNet](https://keras.io/examples/vision/pointnet/)
* [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025)
## Training Dataset
This model was trained on the [ShapeNet dataset](https://shapenet.org/).
The ShapeNet dataset is an ongoing effort to establish a richly-annotated, large-scale dataset of 3D shapes. ShapeNetCore is a subset of the full ShapeNet dataset with clean single 3D models and manually verified category and alignment annotations. It covers 55 common object categories, with about 51,300 unique 3D models.
**Prediction example**
|
jiobiala24/wav2vec2-base-1 | jiobiala24 | 2022-03-03T10:47:28Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:56:08Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-base-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. -->
# wav2vec2-base-1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9254
- Wer: 0.3216
## 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.6597 | 2.2 | 1000 | 0.8904 | 0.5388 |
| 0.4751 | 4.41 | 2000 | 0.7009 | 0.3976 |
| 0.3307 | 6.61 | 3000 | 0.7068 | 0.3672 |
| 0.2574 | 8.81 | 4000 | 0.7320 | 0.3544 |
| 0.2096 | 11.01 | 5000 | 0.7803 | 0.3418 |
| 0.177 | 13.22 | 6000 | 0.7768 | 0.3423 |
| 0.1521 | 15.42 | 7000 | 0.8113 | 0.3375 |
| 0.1338 | 17.62 | 8000 | 0.8153 | 0.3325 |
| 0.1168 | 19.82 | 9000 | 0.8851 | 0.3306 |
| 0.104 | 22.03 | 10000 | 0.8811 | 0.3277 |
| 0.0916 | 24.23 | 11000 | 0.8722 | 0.3254 |
| 0.083 | 26.43 | 12000 | 0.9527 | 0.3265 |
| 0.0766 | 28.63 | 13000 | 0.9254 | 0.3216 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
Johnson-Lsx/Shaoxiong_Lin_dns_ins20_enh_enh_train_enh_dccrn_raw | Johnson-Lsx | 2022-03-03T10:43:01Z | 0 | 0 | espnet | [
"espnet",
"audio",
"audio-to-audio",
"en",
"dataset:dns_ins20",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | audio-to-audio | 2022-03-03T08:35:14Z | ---
tags:
- espnet
- audio
- audio-to-audio
language: en
datasets:
- dns_ins20
license: cc-by-4.0
---
## ESPnet2 ENH model
### `Johnson-Lsx/Shaoxiong_Lin_dns_ins20_enh_enh_train_enh_dccrn_raw`
This model was trained by Shaoxiong Lin using dns_ins20 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 4538462eb7dc6a6b858adcbd3a526fb8173d6f73
pip install -e .
cd egs2/dns_ins20/enh1
./run.sh --skip_data_prep false --skip_train true --download_model Johnson-Lsx/Shaoxiong_Lin_dns_ins20_enh_enh_train_enh_dccrn_raw
```
<!-- Generated by ./scripts/utils/show_enh_score.sh -->
# RESULTS
## Environments
- date: `Thu Feb 10 23:11:40 CST 2022`
- python version: `3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]`
- espnet version: `espnet 0.10.5a1`
- pytorch version: `pytorch 1.9.1`
- Git hash: `6f66283b9eed7b0d5e5643feb18d8f60118a4afc`
- Commit date: `Mon Dec 13 15:30:29 2021 +0800`
## enh_train_enh_dccrn_batch_size_raw
config: ./conf/tuning/train_enh_dccrn_batch_size.yaml
|dataset|STOI|SAR|SDR|SIR|
|---|---|---|---|---|
|enhanced_cv_synthetic|0.98|24.69|24.69|0.00|
|enhanced_tt_synthetic_no_reverb|0.96|17.69|17.69|0.00|
|enhanced_tt_synthetic_with_reverb|0.81|10.45|10.45|0.00|
## ENH config
<details><summary>expand</summary>
```
config: ./conf/tuning/train_enh_dccrn_batch_size.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: chunk
output_dir: exp/enh_train_enh_dccrn_batch_size_raw
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 46366
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: 10
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- si_snr
- max
- - valid
- loss
- min
keep_nbest_models: 1
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 32
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/enh_stats_16k/train/speech_mix_shape
- exp/enh_stats_16k/train/speech_ref1_shape
- exp/enh_stats_16k/train/noise_ref1_shape
valid_shape_file:
- exp/enh_stats_16k/valid/speech_mix_shape
- exp/enh_stats_16k/valid/speech_ref1_shape
- exp/enh_stats_16k/valid/noise_ref1_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 80000
- 80000
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 64000
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/tr_synthetic/wav.scp
- speech_mix
- sound
- - dump/raw/tr_synthetic/spk1.scp
- speech_ref1
- sound
- - dump/raw/tr_synthetic/noise1.scp
- noise_ref1
- sound
valid_data_path_and_name_and_type:
- - dump/raw/cv_synthetic/wav.scp
- speech_mix
- sound
- - dump/raw/cv_synthetic/spk1.scp
- speech_ref1
- sound
- - dump/raw/cv_synthetic/noise1.scp
- noise_ref1
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.001
eps: 1.0e-08
weight_decay: 1.0e-07
scheduler: reducelronplateau
scheduler_conf:
mode: min
factor: 0.7
patience: 1
init: null
model_conf:
loss_type: si_snr
criterions:
# The first criterion
- name: si_snr
conf:
eps: 1.0e-7
# the wrapper for the current criterion
# for single-talker case, we simplely use fixed_order wrapper
wrapper: fixed_order
wrapper_conf:
weight: 1.0
use_preprocessor: false
encoder: stft
encoder_conf:
n_fft: 512
win_length: 400
hop_length: 100
separator: dccrn
separator_conf: {}
decoder: stft
decoder_conf:
n_fft: 512
win_length: 400
hop_length: 100
required:
- output_dir
version: 0.10.5a1
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{ESPnet-SE,
author = {Chenda Li and Jing Shi and Wangyou Zhang and Aswin Shanmugam Subramanian and Xuankai Chang and
Naoyuki Kamo and Moto Hira and Tomoki Hayashi and Christoph B{"{o}}ddeker and Zhuo Chen and Shinji Watanabe},
title = {ESPnet-SE: End-To-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration},
booktitle = {{IEEE} Spoken Language Technology Workshop, {SLT} 2021, Shenzhen, China, January 19-22, 2021},
pages = {785--792},
publisher = {{IEEE}},
year = {2021},
url = {https://doi.org/10.1109/SLT48900.2021.9383615},
doi = {10.1109/SLT48900.2021.9383615},
timestamp = {Mon, 12 Apr 2021 17:08:59 +0200},
biburl = {https://dblp.org/rec/conf/slt/Li0ZSCKHHBC021.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
sattaguru/game | sattaguru | 2022-03-03T05:31:06Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-03T05:30:04Z | https://sattaking-sattaking.com |
shahp7575/electricidad-base-muchocine-finetuned | shahp7575 | 2022-03-03T05:20:16Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"spanish",
"sentiment",
"es",
"dataset:muchocine",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-03T03:46:13Z | ---
language:
- es
tags:
- spanish
- sentiment
datasets:
- muchocine
widget:
- "Increíble pelicula. ¡Altamente recomendado!"
- "Extremadamente malo. Baja calidad"
---
<!-- 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. -->
# electricidad-base-muchocine-finetuned
This model fine-tunes [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on [muchocine](https://huggingface.co/datasets/muchocine) dataset for sentiment classification to predict *star_rating*.
### How to use
The model can be used directly with the HuggingFace `pipeline`.
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("shahp7575/gpt2-horoscopes")
model = AutoModelWithLMHead.from_pretrained("shahp7575/gpt2-horoscopes")
```
### Examples
```python
from transformers import pipeline
clf = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
clf('Esta película es una joya. Todo fue perfecto: historia, casting, dirección. Me encantó el clímax.')
>>> [{'label': '5', 'score': 0.9658033847808838}]
clf("La historia y el casting fueron geniales.")
>>> [{'label': '4', 'score': 0.6666394472122192}]
clf("Me gustó pero podría ser mejor.")
>>> [{'label': '3', 'score': 0.7013391852378845}]
clf("dinero tirado en esta pelicula")
>>> [{'label': '2', 'score': 0.7564149498939514}]
clf("esta película es una película absolutamente repugnante. odio todo al respecto. gastó tanto dinero.")
>>> [{'label': '1', 'score': 0.3040296733379364}]
```
|
algolet/mt5-base-chinese-qg | algolet | 2022-03-03T02:18:05Z | 45 | 17 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | <h3 align="center">
<p>MT5 Base Model for Chinese Question Generation</p>
</h3>
<h3 align="center">
<p>基于mt5的中文问题生成任务</p>
</h3>
#### 可以通过安装question-generation包开始用
```
pip install question-generation
```
使用方法请参考github项目：https://github.com/algolet/question_generation
#### 在线使用
可以直接在线使用我们的模型：https://www.algolet.com/applications/qg
#### 通过transformers调用
``` python
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("algolet/mt5-base-chinese-qg")
model = AutoModelForSeq2SeqLM.from_pretrained("algolet/mt5-base-chinese-qg")
model.eval()
text = "在一个寒冷的冬天，赶集完回家的农夫在路边发现了一条冻僵了的蛇。他很可怜蛇，就把它放在怀里。当他身上的热气把蛇温暖以后，蛇很快苏醒了，露出了残忍的本性，给了农夫致命的伤害——咬了农夫一口。农夫临死之前说：“我竟然救了一条可怜的毒蛇，就应该受到这种报应啊！”"
text = "question generation: " + text
inputs = tokenizer(text,
return_tensors='pt',
truncation=True,
max_length=512)
with torch.no_grad():
outs = model.generate(input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_length=128,
no_repeat_ngram_size=4,
num_beams=4)
question = tokenizer.decode(outs[0], skip_special_tokens=True)
questions = [q.strip() for q in question.split("<sep>") if len(q.strip()) > 0]
print(questions)
['在寒冷的冬天,农夫在哪里发现了一条可怜的蛇?', '农夫是如何看待蛇的?', '当农夫遇到蛇时,他做了什么?']
```
#### 指标
rouge-1: 0.4041
rouge-2: 0.2104
rouge-l: 0.3843
---
language:
- zh
tags:
- mt5
- question generation
metrics:
- rouge
---
|
StivenLancheros/mBERT-base-Biomedical-NER | StivenLancheros | 2022-03-03T00:45:07Z | 22 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-multilingual-cased-finetuned-ner-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-finetuned-ner-4
#This model is part of a test for creating multilingual BioMedical NER systems. Not intended for proffesional use now.
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the CRAFT+BC4CHEMD+BioNLP09 datasets concatenated.
It achieves the following results on the evaluation set:
- Loss: 0.1027
- Precision: 0.9830
- Recall: 0.9832
- F1: 0.9831
- Accuracy: 0.9799
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0658 | 1.0 | 6128 | 0.0751 | 0.9795 | 0.9795 | 0.9795 | 0.9758 |
| 0.0406 | 2.0 | 12256 | 0.0753 | 0.9827 | 0.9815 | 0.9821 | 0.9786 |
| 0.0182 | 3.0 | 18384 | 0.0934 | 0.9834 | 0.9825 | 0.9829 | 0.9796 |
| 0.011 | 4.0 | 24512 | 0.1027 | 0.9830 | 0.9832 | 0.9831 | 0.9799 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
yoavgur/gpt2-bash-history-baseline2 | yoavgur | 2022-03-02T23:43:15Z | 12 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-bash-history-baseline2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-bash-history-baseline2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6480
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 158 | 1.8653 |
| No log | 2.0 | 316 | 1.7574 |
| No log | 3.0 | 474 | 1.6939 |
| 1.9705 | 4.0 | 632 | 1.6597 |
| 1.9705 | 5.0 | 790 | 1.6480 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
Ayham/ernie_gpt2_summarization_cnn_dailymail | Ayham | 2022-03-02T21:43:45Z | 15 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:04Z | ---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: ernie_gpt2_summarization_cnn_dailymail
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. -->
# ernie_gpt2_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
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