---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech
license: cc-by-4.0
---

## ESPnet2 ASR model

### `eml914/streaming_transformer_asr_librispeech`

This model was trained by Emiru Tsunoo using librispeech recipe in [espnet](https://github.com/espnet/espnet/).

### Demo: How to use in ESPnet2

```bash
cd espnet
git checkout 12eb132418a1f69548f7998e53273cd05d989ed9
pip install -e .
cd egs2/librispeech/asr1
./run.sh --skip_data_prep false --skip_train true --download_model eml914/streaming_transformer_asr_librispeech
```

<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Nov 17 18:18:46 JST 2021`
- python version: `3.8.11 (default, Aug  3 2021, 15:09:35)  [GCC 7.5.0]`
- espnet version: `espnet 0.10.5a1`
- pytorch version: `pytorch 1.4.0`
- Git hash: `12eb132418a1f69548f7998e53273cd05d989ed9`
  - Commit date: `Tue Nov 16 10:12:21 2021 +0900`

## asr_train_asr_streaming_fbank_pitch_en_bpe5000_sp
### WER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|97.6|2.2|0.3|0.3|2.7|31.9|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|93.5|5.8|0.7|0.9|7.4|50.4|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|97.5|2.3|0.3|0.3|2.9|33.1|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean_dbg|2620|62|96.8|3.2|0.0|0.0|3.2|0.0|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|93.5|5.7|0.8|0.9|7.4|53.7|

### CER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.2|0.4|0.4|0.3|1.1|31.9|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|97.2|1.6|1.2|0.9|3.7|50.4|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.2|0.4|0.4|0.3|1.1|33.1|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean_dbg|2620|367|99.5|0.0|0.5|0.8|1.4|0.0|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|97.3|1.5|1.3|0.9|3.6|53.7|

### TER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|96.8|2.1|1.1|0.4|3.6|31.9|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|91.9|5.9|2.2|1.5|9.6|50.4|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|96.7|2.2|1.1|0.4|3.7|33.1|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean_dbg|2620|94|97.9|2.1|0.0|1.1|3.2|0.0|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|91.8|5.5|2.7|1.2|9.4|53.7|

## ASR config

<details><summary>expand</summary>

```
config: conf/tuning/train_asr_streaming.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_streaming_fbank_pitch_en_bpe5000_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 33851
dist_launcher: null
multiprocessing_distributed: true
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
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 4
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
unused_parameters: false
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
pretrain_path: null
init_param: []
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 16000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_fbank_pitch_en_bpe5000_sp/train/speech_shape
- exp/asr_stats_fbank_pitch_en_bpe5000_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_fbank_pitch_en_bpe5000_sp/valid/speech_shape
- exp/asr_stats_fbank_pitch_en_bpe5000_sp/valid/text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 800
- 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/fbank_pitch/train_960_sp/feats.scp
    - speech
    - kaldi_ark
-   - dump/fbank_pitch/train_960_sp/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - dump/fbank_pitch/dev/feats.scp
    - speech
    - kaldi_ark
-   - dump/fbank_pitch/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: 0.002
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 25000
token_list:
- <blank>
- <unk>
- ▁THE
- S
- ▁AND
- ▁OF
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- ▁A
- ▁IN
- ▁I
- ▁HE
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- ED
- ▁IT
- ''''
- ▁HIS
- ING
- ▁YOU
- ▁WITH
- ▁FOR
- ▁HAD
- T
- ▁AS
- ▁HER
- ▁IS
- ▁BE
- ▁BUT
- ▁NOT
- ▁SHE
- D
- ▁AT
- ▁ON
- LY
- ▁HIM
- ▁THEY
- ▁ALL
- ▁HAVE
- ▁BY
- ▁SO
- ▁THIS
- ▁MY
- ▁WHICH
- ▁ME
- ▁SAID
- ▁FROM
- ▁ONE
- Y
- E
- ▁WERE
- ▁WE
- ▁NO
- N
- ▁THERE
- ▁OR
- ER
- ▁AN
- ▁WHEN
- ▁ARE
- ▁THEIR
- ▁WOULD
- ▁IF
- ▁WHAT
- ▁THEM
- ▁WHO
- ▁OUT
- M
- ▁DO
- ▁WILL
- ▁UP
- ▁BEEN
- P
- R
- ▁MAN
- ▁THEN
- ▁COULD
- ▁MORE
- C
- ▁INTO
- ▁NOW
- ▁VERY
- ▁YOUR
- ▁SOME
- ▁LITTLE
- ES
- ▁TIME
- RE
- ▁CAN
- ▁LIKE
- LL
- ▁ABOUT
- ▁HAS
- ▁THAN
- ▁DID
- ▁UPON
- ▁OVER
- IN
- ▁ANY
- ▁WELL
- ▁ONLY
- B
- ▁SEE
- ▁GOOD
- ▁OTHER
- ▁TWO
- L
- ▁KNOW
- ▁GO
- ▁DOWN
- ▁BEFORE
- A
- AL
- ▁OUR
- ▁OLD
- ▁SHOULD
- ▁MADE
- ▁AFTER
- ▁GREAT
- ▁DAY
- ▁MUST
- ▁COME
- ▁HOW
- ▁SUCH
- ▁CAME
- LE
- ▁WHERE
- ▁US
- ▁NEVER
- ▁THESE
- ▁MUCH
- ▁DE
- ▁MISTER
- ▁WAY
- G
- ▁S
- ▁MAY
- ATION
- ▁LONG
- OR
- ▁AM
- ▁FIRST
- ▁BACK
- ▁OWN
- ▁RE
- ▁AGAIN
- ▁SAY
- ▁MEN
- ▁WENT
- ▁HIMSELF
- ▁HERE
- NESS
- ▁THINK
- V
- IC
- ▁EVEN
- ▁THOUGHT
- ▁HAND
- ▁JUST
- ▁O
- ▁UN
- VE
- ION
- ▁ITS
- 'ON'
- ▁MAKE
- ▁MIGHT
- ▁TOO
- K
- ▁AWAY
- ▁LIFE
- TH
- ▁WITHOUT
- ST
- ▁THROUGH
- ▁MOST
- ▁TAKE
- ▁DON
- ▁EVERY
- F
- O
- ▁SHALL
- ▁THOSE
- ▁EYES
- AR
- ▁STILL
- ▁LAST
- ▁HOUSE
- ▁HEAD
- ABLE
- ▁NOTHING
- ▁NIGHT
- ITY
- ▁LET
- ▁MANY
- ▁OFF
- ▁BEING
- ▁FOUND
- ▁WHILE
- EN
- ▁SAW
- ▁GET
- ▁PEOPLE
- ▁FACE
- ▁YOUNG
- CH
- ▁UNDER
- ▁ONCE
- ▁TELL
- AN
- ▁THREE
- ▁PLACE
- ▁ROOM
- ▁YET
- ▁SAME
- IL
- US
- U
- ▁FATHER
- ▁RIGHT
- EL
- ▁THOUGH
- ▁ANOTHER
- LI
- RI
- ▁HEART
- IT
- ▁PUT
- ▁TOOK
- ▁GIVE
- ▁EVER
- ▁E
- ▁PART
- ▁WORK
- ERS
- ▁LOOK
- ▁NEW
- ▁KING
- ▁MISSUS
- ▁SIR
- ▁LOVE
- ▁MIND
- ▁LOOKED
- W
- RY
- ▁ASKED
- ▁LEFT
- ET
- ▁LIGHT
- CK
- ▁DOOR
- ▁MOMENT
- RO
- ▁WORLD
- ▁THINGS
- ▁HOME
- UL
- ▁THING
- LA
- ▁WHY
- ▁MOTHER
- ▁ALWAYS
- ▁FAR
- FUL
- ▁WATER
- CE
- IVE
- UR
- ▁HEARD
- ▁SOMETHING
- ▁SEEMED
- I
- LO
- ▁BECAUSE
- OL
- ▁END
- ▁TOLD
- ▁CON
- ▁YES
- ▁GOING
- ▁GOT
- RA
- IR
- ▁WOMAN
- ▁GOD
- EST
- TED
- ▁FIND
- ▁KNEW
- ▁SOON
- ▁EACH
- ▁SIDE
- H
- TON
- MENT
- ▁OH
- NE
- Z
- LING
- ▁AGAINST
- TER
- ▁NAME
- ▁MISS
- ▁QUITE
- ▁WANT
- ▁YEARS
- ▁FEW
- ▁BETTER
- ENT
- ▁HALF
- ▁DONE
- ▁ALSO
- ▁BEGAN
- ▁HAVING
- ▁ENOUGH
- IS
- ▁LADY
- ▁WHOLE
- LESS
- ▁BOTH
- ▁SEEN
- ▁SET
- ▁WHITE
- ▁COURSE
- IES
- ▁VOICE
- ▁CALLED
- ▁D
- ▁EX
- ATE
- ▁TURNED
- ▁GAVE
- ▁C
- ▁POOR
- MAN
- UT
- NA
- ▁DEAR
- ISH
- ▁GIRL
- ▁MORNING
- ▁BETWEEN
- LED
- ▁NOR
- IA
- ▁AMONG
- MA
- ▁
- ▁SMALL
- ▁REST
- ▁WHOM
- ▁FELT
- ▁HANDS
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- ▁HIGH
- ▁M
- ▁HOWEVER
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- ▁P
- CO
- ▁STOOD
- ID
- ▁KIND
- ▁HUNDRED
- AS
- ▁ROUND
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- TY
- ▁SINCE
- ▁G
- AM
- ▁LA
- SE
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- ▁MA
- ▁PERHAPS
- ▁WORDS
- ATED
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- X
- ▁MO
- ▁SAT
- ▁REPLIED
- ▁FOUR
- ▁ANYTHING
- ▁TILL
- ▁UNTIL
- ▁BLACK
- TION
- ▁CRIED
- RU
- TE
- ▁FACT
- ▁HELP
- ▁NEXT
- ▁LOOKING
- ▁DOES
- ▁FRIEND
- ▁LAY
- ANCE
- ▁POWER
- ▁BROUGHT
- VER
- ▁FIRE
- ▁KEEP
- PO
- FF
- ▁COUNTRY
- ▁SEA
- ▁WORD
- ▁CAR
- ▁DAYS
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- ▁IMP
- ▁REASON
- KE
- ▁INDEED
- TING
- ▁MATTER
- ▁FULL
- ▁TEN
- TIC
- ▁LAND
- ▁RATHER
- ▁AIR
- ▁HOPE
- ▁DA
- ▁OPEN
- ▁FEET
- ▁EN
- ▁FIVE
- ▁POINT
- ▁CO
- OM
- ▁LARGE
- ▁B
- ▁CL
- ME
- ▁GONE
- ▁CHILD
- INE
- GG
- ▁BEST
- ▁DIS
- UM
- ▁HARD
- ▁LORD
- OUS
- ▁WIFE
- ▁SURE
- ▁FORM
- DE
- ▁DEATH
- ANT
- ▁NATURE
- ▁BA
- ▁CARE
- ▁BELIEVE
- PP
- ▁NEAR
- ▁RO
- ▁RED
- ▁WAR
- IE
- ▁SPEAK
- ▁FEAR
- ▁CASE
- ▁TAKEN
- ▁ALONG
- ▁CANNOT
- ▁HEAR
- ▁THEMSELVES
- CI
- ▁PRESENT
- AD
- ▁MASTER
- ▁SON
- ▁THUS
- ▁LI
- ▁LESS
- ▁SUN
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- IM
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- ▁THOUSAND
- ▁MONEY
- ▁W
- ▁BEHIND
- ▁CHILDREN
- ▁DOCTOR
- AC
- ▁TWENTY
- ▁WISH
- ▁SOUND
- ▁WHOSE
- ▁LEAVE
- ▁ANSWERED
- ▁THOU
- ▁DUR
- ▁HA
- ▁CERTAIN
- ▁PO
- ▁PASSED
- GE
- TO
- ▁ARM
- ▁LO
- ▁STATE
- ▁ALONE
- TA
- ▁SHOW
- ▁NEED
- ▁LIVE
- ND
- ▁DEAD
- ENCE
- ▁STRONG
- ▁PRE
- ▁TI
- ▁GROUND
- SH
- TI
- ▁SHORT
- IAN
- UN
- ▁PRO
- ▁HORSE
- MI
- ▁PRINCE
- ARD
- ▁FELL
- ▁ORDER
- ▁CALL
- AT
- ▁GIVEN
- ▁DARK
- ▁THEREFORE
- ▁CLOSE
- ▁BODY
- ▁OTHERS
- ▁SENT
- ▁SECOND
- ▁OFTEN
- ▁CA
- ▁MANNER
- MO
- NI
- ▁BRING
- ▁QUESTION
- ▁HOUR
- ▁BO
- AGE
- ▁ST
- ▁TURN
- ▁TABLE
- ▁GENERAL
- ▁EARTH
- ▁BED
- ▁REALLY
- ▁SIX
- 'NO'
- IST
- ▁BECOME
- ▁USE
- ▁READ
- ▁SE
- ▁VI
- ▁COMING
- ▁EVERYTHING
- ▁EM
- ▁ABOVE
- ▁EVENING
- ▁BEAUTIFUL
- ▁FEEL
- ▁RAN
- ▁LEAST
- ▁LAW
- ▁ALREADY
- ▁MEAN
- ▁ROSE
- WARD
- ▁ITSELF
- ▁SOUL
- ▁SUDDENLY
- ▁AROUND
- RED
- ▁ANSWER
- ICAL
- ▁RA
- ▁WIND
- ▁FINE
- ▁WON
- ▁WHETHER
- ▁KNOWN
- BER
- NG
- ▁TA
- ▁CAPTAIN
- ▁EYE
- ▁PERSON
- ▁WOMEN
- ▁SORT
- ▁ASK
- ▁BROTHER
- ▁USED
- ▁HELD
- ▁BIG
- ▁RETURNED
- ▁STRANGE
- ▁BU
- ▁PER
- ▁FREE
- ▁EITHER
- ▁WITHIN
- ▁DOUBT
- ▁YEAR
- ▁CLEAR
- ▁SIGHT
- ▁GRA
- ▁LOST
- ▁KEPT
- ▁F
- PE
- ▁BAR
- ▁TOWN
- ▁SLEEP
- ARY
- ▁HAIR
- ▁FRIENDS
- ▁DREAM
- ▁FELLOW
- PER
- ▁DEEP
- QUE
- ▁BECAME
- ▁REAL
- ▁PAST
- ▁MAKING
- RING
- ▁COMP
- ▁ACT
- ▁BAD
- HO
- STER
- ▁YE
- ▁MEANS
- ▁RUN
- MEN
- ▁DAUGHTER
- ▁SENSE
- ▁CITY
- ▁SOMETIMES
- ▁TOWARDS
- ▁ROAD
- ▁SP
- ▁LU
- ▁READY
- ▁FOOT
- ▁COLD
- ▁SA
- ▁LETTER
- ▁ELSE
- ▁MAR
- ▁STA
- BE
- ▁TRUTH
- ▁LE
- BO
- ▁BUSINESS
- CHE
- ▁JOHN
- ▁SUBJECT
- ▁COURT
- ▁IDEA
- ILY
- ▁RIVER
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- ▁FAMILY
- HE
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- ▁GLAD
- ▁SEVERAL
- IAL
- ▁UNDERSTAND
- ▁SC
- ▁POSSIBLE
- ▁DIFFERENT
- ▁RETURN
- ▁ARMS
- ▁LOW
- ▁HOLD
- ▁TALK
- ▁RU
- ▁WINDOW
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- ▁SH
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- ▁CAP
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- ▁CAUSE
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- ▁GA
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- ▁TIMES
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- ▁ILL
- ▁ART
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- ▁TROUBLE
- ▁PAIN
- ▁CONTINUED
- ▁SCHOOL
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- ▁CARRIED
- ▁SAYING
- HA
- ▁CHANGE
- ▁FOLLOW
- ▁GOLD
- ▁SW
- ▁FEELING
- ▁COMMAND
- ▁BEAR
- ▁CERTAINLY
- ▁BLUE
- ▁NE
- CA
- ▁WILD
- ▁ACCOUNT
- ▁OUGHT
- UD
- ▁T
- ▁BREATH
- ▁WANTED
- ▁RI
- ▁HEAVEN
- ▁PURPOSE
- ▁CHARACTER
- ▁RICH
- ▁PE
- ▁DRESS
- OS
- FA
- ▁TH
- ▁ENGLISH
- ▁CHANCE
- ▁SHIP
- ▁VIEW
- ▁TOWARD
- AK
- ▁JOY
- ▁JA
- ▁HAR
- ▁NEITHER
- ▁FORCE
- ▁UNCLE
- DER
- ▁PLAN
- ▁PRINCESS
- DI
- ▁CHIEF
- ▁HAT
- ▁LIVED
- ▁AB
- ▁VISIT
- ▁MOR
- TEN
- ▁WALL
- UC
- ▁MINE
- ▁PLEASURE
- ▁SMILE
- ▁FRONT
- ▁HU
- ▁DEAL
- OW
- ▁FURTHER
- GED
- ▁TRIED
- DA
- VA
- ▁NONE
- ▁ENTERED
- ▁QUEEN
- ▁PAY
- ▁EL
- ▁EXCEPT
- ▁SHA
- ▁FORWARD
- ▁EIGHT
- ▁ADDED
- ▁PUBLIC
- ▁EIGHTEEN
- ▁STAR
- ▁HAPPENED
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- OGRAPH
- ▁SUMMON
- ▁JULIA
- ▁FLOOD
- ▁WAL
- ULATION
- ▁SLIGHTLY
- ▁LODGE
- ▁WIRE
- ▁CONFUSION
- ▁UNEXPECTED
- ▁CONCEIVE
- ▁PRIZE
- ▁JESUS
- ▁ADDITION
- ▁RUDE
- ▁FATAL
- ▁CARELESS
- ▁PATCH
- ▁KO
- ▁CATHERINE
- ▁PARLIAMENT
- ▁PROFOUND
- ▁ALOUD
- ▁RELIEVE
- ▁PUSH
- ABILITY
- ▁ACCOMPANIED
- ▁SOVEREIGN
- ▁SINGULAR
- ▁ECHO
- ▁COMPOSED
- ▁SHAKING
- ATORY
- ▁ASSISTANCE
- ▁TEACHER
- ▁HORRIBLE
- ▁STRICT
- ▁VERSE
- ▁PUNISHMENT
- ▁GOWN
- ▁MISTAKEN
- ▁VARI
- ▁SWEPT
- ▁GESTURE
- ▁BUSH
- ▁STEEL
- ▁AFFECTED
- ▁DIRECTED
- ▁SURROUNDED
- ▁ABSURD
- ▁SUGAR
- ▁SCRAP
- ▁IMMEDIATE
- ▁SADDLE
- ▁TY
- ▁ARISE
- ▁SIGHED
- ▁EXCHANGE
- ▁IMPATIENT
- ▁SNAP
- ▁EMBRACE
- ▁DISEASE
- ▁PROFIT
- ▁RIDING
- ▁RECOVERED
- ▁GOVERN
- ▁STRETCH
- ▁CONVINCED
- ▁LEANING
- ▁DOMESTIC
- ▁COMPLEX
- ▁MANIFEST
- ▁INDULGE
- ▁GENIUS
- ▁AGENT
- ▁VEIL
- ▁DESCRIPTION
- ▁INCLINED
- ▁DECEIVE
- ▁DARLING
- ▁REIGN
- HU
- ▁ENORMOUS
- ▁RESTRAIN
- ▁DUTIES
- BURY
- TTERED
- ▁POLE
- ▁ENABLE
- ▁EXCEPTION
- ▁INTIMATE
- ▁COUNTESS
- ▁TRIBE
- ▁HANDKERCHIEF
- ▁MIDNIGHT
- ▁PROBLEM
- ▁TRAMP
- ▁OIL
- CAST
- ▁CRUSH
- ▁DISCUSS
- ▁RAM
- ▁TROT
- ▁UNRE
- ▁WHIRL
- ▁LOCKED
- ▁HORIZON
- ▁OFFICIAL
- ▁SCHEME
- ▁DROWN
- ▁PIERRE
- ▁PERMITTED
- ▁CONNECTED
- ▁ASSURE
- ▁COCK
- ▁UTMOST
- ▁DEVOTED
- ▁RELI
- ▁SUFFICIENTLY
- ▁INTELLECTUAL
- ▁CARPET
- ▁OBJECTION
- ▁AFTERWARD
- ▁REALITY
- ▁NEGRO
- ▁RETAIN
- ▁ASCEND
- ▁CEASE
- ▁KATE
- ▁MARVEL
- KO
- ▁BOND
- MOST
- ▁COAL
- GATE
- ▁IGNORANT
- ▁BREAKING
- ▁TWIN
- ▁ASTONISHMENT
- ▁COFFEE
- ▁JAR
- ▁CITIES
- ▁ORIGIN
- ▁EXECUT
- ▁FINAL
- ▁INHABITANTS
- ▁STABLE
- ▁CHIN
- ▁PARTIES
- ▁PLUNGE
- ▁GENEROUS
- ▁DESCRIBE
- ▁ANNOUNCED
- ▁MERIT
- ▁REVERE
- ▁ERE
- ACIOUS
- ZI
- ▁DISAPPOINT
- ▁SUGGESTION
- ▁DOUBTLESS
- ▁TRUNK
- ▁STAMP
- ▁JOB
- ▁APPOINTED
- ▁DIVIDED
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- CHI
- ▁ABSOLUTE
- ▁FEARFUL
- ▁PRIVILEGE
- ▁CRAFT
- ▁STEEP
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- ▁CONSTRUCT
- ▁EMPIRE
- ▁EXPEDITION
- ▁ERECT
- ▁OFFEND
- ▁INTEND
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- ▁DESTROYED
- ▁CONTRACT
- ▁THIRST
- ▁WAGON
- ▁EVA
- ▁GLOOM
- ▁ATMOSPHERE
- ▁RESERVE
- ▁VOTE
- ▁GER
- ▁NONSENSE
- ▁PREVAIL
- ▁QUALITY
- ▁CLASP
- ▁CONCLUDED
- ▁RAP
- ▁KATY
- ▁ETERNAL
- ▁MUTTERED
- ▁NEGLECT
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- LOCK
- ▁ELECTRIC
- ▁HAY
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- ▁LEAF
- ▁FAILURE
- WICK
- ▁JEAN
- ▁NUMEROUS
- ▁INFANT
- ▁REALIZED
- ▁TRAVELLER
- ▁HUNGER
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- ▁RECOMMEND
- ▁CREP
- ZZLE
- ▁RICHARD
- WORK
- ▁MONTE
- ▁PREACH
- ▁PALM
- AVI
- ▁ANYWHERE
- ▁DISPOSITION
- ▁MIRROR
- ▁VENTURE
- ▁POUND
- ▁CIGAR
- ▁INVITED
- ▁BENCH
- ▁PROTECTION
- ▁BENEFIT
- ▁THOMAS
- ▁CLERK
- ▁REPROACH
- ▁UNIFORM
- ▁GENERATION
- ▁SEAL
- ▁COMPASS
- ▁WARNING
- ▁EXTENDED
- ▁DIFFICULTIES
- ▁MAYBE
- ▁GROAN
- ▁AFFECT
- ▁COMB
- ▁EARN
- ▁WESTERN
- ▁IDLE
- ▁SCORE
- ▁TAP
- ▁ASTONISHED
- ▁INTRODUCED
- ▁LEISURE
- ▁LIEUTENANT
- ▁VIOLENCE
- ▁FIRMLY
- ▁MONSTER
- ▁UR
- ▁PROPERLY
- ▁TWIST
- ▁PIRATE
- ▁ROBBER
- ▁BATTER
- ▁WEPT
- ▁LEANED
- ▁FOG
- ▁ORNAMENT
- ▁ANDREW
- ▁BUSHES
- ▁REPUBLIC
- ▁CONFIDENT
- ▁LEAN
- ▁DART
- ▁STOOP
- ▁CURL
- ▁COUNTER
- ▁NORTHERN
- ▁PEARL
- ▁NEAREST
- ▁FRANCIS
- ▁WANDERING
- ▁FREQUENT
- ▁STARTLED
- ▁STATEMENT
- ▁OCCUR
- ▁BLOOM
- ▁NERVE
- ▁INSPECT
- ▁INDUCE
- ▁FLATTER
- ▁DATE
- ▁AMBITION
- ▁SLOPE
- ▁MALE
- ▁MADAM
- ▁MONK
- ▁RENT
- ▁CONFIRM
- ▁INVESTIGAT
- ▁RABBIT
- ▁REGIMENT
- ▁SUBMIT
- ▁SPELL
- ▁FURIOUS
- ▁RAIL
- ▁BESTOW
- ▁RALPH
- ▁SCATTERED
- ▁COMPELLED
- ▁THREAD
- ▁CHILL
- ▁DENY
- ▁PRONOUNC
- ▁MANKIND
- ▁CATTLE
- ▁EXECUTION
- ▁REBEL
- ▁SUPREME
- ▁VALUABLE
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- ▁CONVEY
- ▁TIDE
- ▁GLOOMY
- ▁COIN
- ▁ACTUAL
- ▁TAX
- ▁PROVINCE
- ▁GRATEFUL
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- ▁VANISHED
- ▁DIANA
- ▁HAUNT
- ▁DRAGON
- ▁CRAWL
- ▁CHINA
- ▁GRATITUDE
- ▁NEAT
- ▁FINISH
- ▁INTENT
- ▁FRIGHT
- ▁EMBARRASS
- ▁THIRTEEN
- ▁RUTH
- ▁SLIGHTEST
- ▁DEVELOPMENT
- ▁INTERVIEW
- ▁SPECTACLE
- ▁BROOK
- VIE
- ▁WEAKNESS
- ▁AUDIENCE
- ▁CONSEQUENTLY
- ▁ABROAD
- ▁ASPECT
- ▁PAINTED
- ▁RELEASE
- ▁INSULT
- ▁SOOTH
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- ▁EMERG
- ▁BRIG
- ▁ESTEEM
- ▁INVITATION
- ▁PASSENGER
- ▁PUBLISH
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- ▁IRISH
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- ▁BEATEN
- ▁FIFTH
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- ▁EATEN
- ▁PURPLE
- ▁COMMITTED
- ▁COUNTRIES
- ▁PERCEIVE
- ISON
- ▁CELEBRAT
- ▁GRANDMOTHER
- ▁SHUDDER
- ▁SUNSHINE
- ▁SPANISH
- ▁HITHERTO
- ▁MARILLA
- ▁SNAKE
- ▁MOCK
- ▁INTERFERE
- ▁WALTER
- ▁AMID
- ▁MARBLE
- ▁MISSION
- TERIOR
- ▁DRIVING
- ▁FURNITURE
- ▁STEADY
- ▁CIRCUMSTANCE
- ▁INTERPRET
- ▁ENCHANT
- ▁ERROR
- ▁CONVICTION
- ▁HELPLESS
- ▁MEDICINE
- ▁QUALITIES
- ▁ITALIAN
- ▁HASTENED
- ▁OCCASIONALLY
- ▁PURSUED
- ▁HESITATED
- ▁INDEPENDENT
- ▁OLIVER
- ▁LINGER
- UX
- ▁EXAMINED
- ▁REPENT
- ▁PHYSICIAN
- ▁CHASE
- ▁BELOVED
- ▁ATTACHED
- ▁FLORENCE
- ▁HONEY
- ▁MOUSE
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- ▁FULFIL
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- ▁TRISTRAM
- ▁FANCIED
- ▁EXCESS
- ▁CURSE
- ▁CHU
- ▁QUANTITY
- ▁THORNTON
- ▁CREATED
- ▁CONTINUALLY
- ▁LIGHTNING
- ▁BORNE
- ▁TOTAL
- ▁DISPOSED
- ▁RIFLE
- ▁POLLY
- ▁GOAT
- ▁BACKWARD
- ▁VIRGINIA
- ▁KICK
- ▁PERIL
- ▁QUO
- ▁GLORIOUS
- ▁MULTITUDE
- ▁LEATHER
- ▁ABSENT
- ▁DEMON
- ▁DEBT
- ▁TORTURE
- ▁ACCORD
- ▁MATE
- ▁CATHOLIC
- ▁PILL
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- ▁DEAREST
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- ▁TEMPT
- ▁STEADILY
- ▁DISGUST
- ▁SILLY
- ▁ARRIVE
- ▁DRANK
- ▁LEVI
- ▁COMMUNICAT
- ▁RACHEL
- ▁WASHINGTON
- ▁RESIGN
- ▁MEANTIME
- ▁LACE
- ▁ENGAGEMENT
- ▁QUIVER
- ▁SEPARATED
- ▁DISCUSSION
- ▁VENTURED
- ▁SURROUNDING
- ▁POLISH
- ▁NAIL
- ▁SWELL
- ▁JOKE
- ▁LINCOLN
- ▁STUDENT
- ▁GLITTER
- ▁RUSSIAN
- ▁READILY
- ▁CHRIS
- ▁POVERTY
- ▁DISGRACE
- ▁CHEESE
- ▁HEAVILY
- ▁SCALE
- ▁STAFF
- ▁ENTREAT
- ▁FAREWELL
- ▁LUNCH
- ▁PEEP
- ▁MULE
- ▁SOMEONE
- ▁DISAPPEAR
- ▁DECISION
- ▁PISTOL
- ▁PUN
- ▁SPUR
- ▁ASSUMED
- ▁EXTEND
- ▁ENTHUSIASM
- ▁DEFINITE
- ▁UNDERTAKE
- ▁COMMITTEE
- ▁SIMON
- ▁FENCE
- ▁APPLIED
- ▁RELATED
- ▁VICE
- ▁UNPLEASANT
- ▁PROBABLE
- ▁PROCURE
- ▁FROWN
- ▁CLOAK
- ▁HUMANITY
- ▁FAMILIES
- ▁PHILOSOPHER
- ▁DWARF
- ▁OVERCOME
- ▁DEFEAT
- ▁FASTENED
- ▁MARSH
- ▁CLASSES
- ▁TOMB
- ▁GRACIOUS
- ▁REMOTE
- ▁CELL
- ▁SHRIEK
- ▁RESCUE
- ▁POOL
- ▁ORGANIZ
- ▁CHOSE
- ▁CUTTING
- ▁COWARD
- ▁BORDER
- ▁DIRTY
- ▁MONKEY
- ▁HOOK
- ▁CHUCK
- ▁EMILY
- ▁JEST
- ▁PLAC
- ▁WEIGH
- ▁ASSOCIATE
- ▁GLIMPSE
- ▁STUCK
- ▁BOLT
- ▁MURDERER
- ▁PONY
- ▁DISTINGUISH
- ▁INSTITUTION
- ▁CUNNING
- ▁COMPLIMENT
- ▁APPETITE
- ▁REPUTATION
- ▁FEEBLE
- ▁KIN
- ▁SERIES
- ▁GRACEFUL
- ▁PLATFORM
- ▁BREEZE
- ▁PHRASE
- ▁CLAY
- MONT
- ▁RATTL
- ▁OPPOSITION
- ▁LANE
- ▁BOAST
- ▁GROWTH
- ▁INCLINATION
- ▁BEHAVE
- ▁SUSAN
- ▁DISTINCTION
- ▁DISLIKE
- ▁NICHOLAS
- ▁SATISFY
- ▁DRAMA
- ▁ELBOW
- ▁GAZING
- ▁CONSUM
- ▁SPIN
- ▁OATH
- ▁CHANNEL
- ▁CHARACTERISTIC
- ▁SPEAR
- ▁SLAIN
- ▁SAUCE
- ▁FROG
- ▁CONCEPTION
- ▁TIMID
- ▁ZEAL
- ▁APPARENT
- SHIRE
- ▁CENTER
- ▁VARIETY
- ▁DUSK
- ▁APT
- ▁COLUMN
- ▁REVENGE
- ▁RIVAL
- ▁IMITAT
- ▁PASSIONATE
- ▁SELFISH
- ▁NORMAN
- ▁REPAIR
- ▁THRILL
- ▁TREATMENT
- ▁ROSA
- ▁MARTIN
- ▁INDIFFERENT
- ▁THITHER
- ▁GALLANT
- ▁PEPPER
- ▁RECOLLECT
- ▁VINE
- ▁SCARCE
- ▁SHIELD
- ▁MINGLED
- CLOSE
- ▁HARSH
- ▁BRICK
- ▁HUMOR
- ▁MISCHIEF
- ▁TREMENDOUS
- ▁FUNCTION
- ▁SMART
- ▁SULTAN
- ▁DISMISS
- ▁THREATENED
- ▁CHEAP
- ▁FLOCK
- ▁ENDEAVOR
- ▁WHISK
- ▁ITALY
- ▁WAIST
- ▁FLUTTER
- ▁SMOKING
- ▁MONARCH
- ▁AFRICA
- ▁ACCUSE
- ▁HERBERT
- ▁REFRESH
- ▁REJOICE
- ▁PILLOW
- ▁EXPECTATION
- ▁POETRY
- ▁HOPELESS
- ▁PERISH
- ▁PHILOSOPHY
- ▁WHISTLE
- ▁BERNARD
- ▁LAMENT
- ▁IMPROVE
- ▁SUP
- ▁PERPLEX
- ▁FOUNTAIN
- ▁LEAGUE
- ▁DESPISE
- ▁IGNORANCE
- ▁REFERENCE
- ▁DUCK
- ▁GROVE
- ▁PURSE
- ▁PARTNER
- ▁PROPHET
- ▁SHIVER
- ▁NEIGHBOURHOOD
- ▁REPRESENTATIVE
- SAIL
- ▁WIP
- ▁ACQUIRED
- ▁CHIMNEY
- ▁DOCTRINE
- ▁MAXIM
- ▁ANGLE
- ▁MAJORITY
- ▁AUTUMN
- ▁CONFUSED
- ▁CRISTO
- ▁ACHIEVE
- ▁DISGUISE
- ▁REDUCED
- ▁EARLIER
- ▁THEATRE
- ▁DECIDE
- MINATED
- OLOGICAL
- ▁OCCUPATION
- ▁VIGOROUS
- ▁CONTINENT
- ▁DECLINE
- ▁COMMUNITY
- ▁MOTIONLESS
- ▁HATRED
- ▁COMMUNICATION
- ▁BOWL
- ▁COMMENT
- ▁APPROVE
- ▁CEREMONY
- ▁CRIMINAL
- ▁SCIENTIFIC
- ▁DUCHESS
- ▁VIVID
- ▁SHIFT
- ▁AVAIL
- ▁DAMP
- ▁JOHNSON
- ▁SLENDER
- ▁CONTRAST
- ▁AMUSEMENT
- ▁PLOT
- ▁LYN
- ▁ASSOCIATION
- ▁SNATCH
- ▁UNCERTAIN
- ▁PRESSURE
- ▁PERCH
- ▁APPLY
- ▁PLANET
- ▁NOTWITHSTANDING
- ▁SWUNG
- ▁STIRRED
- ▁ATTENDANT
- ▁ENJOYMENT
- ▁WORRY
- ▁ALBERT
- ▁NAKED
- ▁TALENT
- ▁MARIAN
- ▁REFORM
- ▁DELIBERATE
- ▁INTELLIGENT
- ▁SENSITIVE
- ▁YONDER
- ▁PUPIL
- ▁FRIGHTFUL
- ▁DOUBTFUL
- ▁STANDARD
- ▁MAGISTRATE
- ▁SHEPHERD
- ▁STOMACH
- ▁DEPOSIT
- ▁RENEW
- ▁HEDGE
- ▁FRANCS
- ▁POSSIBILITY
- ▁RESEMBLE
- ▁FATIGUE
- ▁PORTRAIT
- ▁FAVORITE
- ▁CREAM
- ▁BURG
- ▁SECRETARY
- ▁DIVERS
- ▁ACTIVITY
- ▁SPECULAT
- ▁HUMOUR
- ▁FITTED
- ▁EXTERNAL
- ▁CETERA
- ▁WRAPPED
- ▁WHIT
- ▁FRED
- ▁EXAMINATION
- ▁LODGING
- ▁OWING
- ▁JAW
- ▁CROW
- ▁BALANCE
- ▁PUFF
- ▁TENDERNESS
- ▁PORTHOS
- ▁ANCHOR
- ▁INTERRUPT
- ▁NECESSARILY
- ▁PERPETUAL
- ▁AGONY
- ▁POPE
- ▁SCHOLAR
- ▁SCOTLAND
- ▁SUPPRESS
- ▁WRATH
- ▁WRECK
- ▁EXCEED
- ▁PERFECTION
- ▁INDIA
- ▁TRADITION
- ▁SECTION
- ▁EASTERN
- ▁DOORWAY
- ▁WIVES
- ▁CONVENTION
- ▁ANNOUNC
- ▁EGYPT
- ▁CONTRADICT
- ▁SCRATCH
- ▁CENTRAL
- ▁GLOVE
- ▁WAX
- ▁PREPARE
- ▁ACCOMPANY
- ▁INCREASING
- ▁LIBERAL
- ▁RAISING
- ▁ORANGE
- ▁SHOE
- ▁ATTRIBUTE
- ▁LITERATURE
- ▁PUZZLED
- ▁WITHDRAW
- ▁WHITHER
- ▁HAWK
- ▁MOONLIGHT
- ▁EXAMINE
- ▁HAPPILY
- ▁PRECEDE
- ▁DETECTIVE
- ▁INCHES
- ▁SOLITARY
- ▁DUTCH
- ▁NAPOLEON
- ▁UNEASY
- ▁CARDINAL
- ▁BLEW
- ▁FOWL
- ▁DECORAT
- ▁CHILDHOOD
- ▁TORMENT
- ▁LOSING
- ▁PERMISSION
- ▁BLANK
- ▁UPSTAIRS
- ▁CAPACITY
- ▁TRIFLE
- ▁FOLLY
- ▁RECOGNIZE
- ▁REMOVE
- ▁VENGEANCE
- ▁ENTERPRISE
- ▁BEDROOM
- ▁ANYHOW
- ▁INQUIRY
- ▁ASHES
- ▁DRAG
- ▁HUSH
- ▁AWKWARD
- ▁SATURDAY
- ▁GENUINE
- ▁SURVIV
- ▁SKIRT
- ▁AFFECTIONATE
- ▁TANG
- ▁MUTUAL
- ▁DISPUTE
- ▁EAGLE
- ▁INCOME
- ▁BIND
- ▁FAME
- ▁IMPROVEMENT
- ROVING
- ▁DIFFER
- ▁AWOKE
- ▁SLEEVE
- ▁SOLITUDE
- ▁FAVOURITE
- JI
- ▁DETECT
- ▁COMPREHEND
- ▁PREPARING
- ▁SERPENT
- ▁SUMMIT
- ▁KNOT
- ▁KNIT
- ▁COPY
- ▁STOPPING
- ▁FADED
- ▁HIDEOUS
- ▁JULIE
- STEAD
- ▁SHINE
- ▁CONFLICT
- ▁PROPOSITION
- ▁REFUGE
- ▁GALLERY
- ▁BUNDLE
- ▁AXE
- ▁SLAVERY
- ▁MASK
- ▁ALYOSHA
- ▁LADDER
- ▁DEPARTMENT
- ▁DISCHARGE
- ▁DEPRESS
- ▁GALLOP
- ▁SCARLET
- ▁KITTY
- ▁RECEIVING
- ▁SURRENDER
- ▁SUSTAIN
- ▁TWILIGHT
- ▁CONGRESS
- ▁IRELAND
- ▁FUNNY
- ▁LEND
- ▁CONSTITUTE
- ▁FUNERAL
- ▁CRYSTAL
- ▁SPAIN
- ▁EXCEEDINGLY
- ▁DAMN
- ▁COMMUN
- ▁CIVILIZATION
- ▁PREJUDICE
- ▁PORCH
- ▁ASSISTANT
- ▁INDUSTRY
- ▁TUMBLE
- ▁DEFENCE
- ▁HITHER
- ▁SMOT
- ▁COLONI
- ▁AMAZEMENT
- ▁MARGUERITE
- ▁MIRACLE
- ▁INHERIT
- ▁BEGGAR
- ▁ENVELOPE
- ▁INDIGNATION
- ▁NATASHA
- ▁PROPOSAL
- ▁FRAGMENT
- ▁ROUSED
- ▁ROAST
- ENCIES
- ▁COMMENCED
- ▁RESOURCE
- ▁POPULATION
- ▁QUOTH
- ▁PURSUE
- ▁EDUCAT
- ▁AFFLICT
- ▁CONTACT
- ▁CRIMSON
- ▁DIVISION
- ▁DISORDER
- ▁COPPER
- ▁SOLICIT
- ▁MODERATE
- ▁DRUM
- ▁SWIM
- ▁SALUTE
- ▁ASSUME
- ▁MUSCLE
- ▁OVERWHELM
- ▁SHAKESPEARE
- ▁STRUGGLING
- ▁TRANQUIL
- ▁CHICKEN
- ▁TREAD
- ▁CLAW
- ▁BIBLE
- ▁RIDGE
- ▁THREAT
- ▁VELVET
- ▁EXPOSED
- ▁IDIOT
- ▁BARREL
- ▁PENNY
- ▁TEMPTATION
- ▁DANGLARS
- ▁CENTURIES
- ▁DISTRIBUT
- ▁REJECT
- ▁RETORTED
- ▁CONCENTRAT
- ▁CORDIAL
- ▁MOTOR
- ▁CANNON
- KEEP
- ▁WRETCH
- ▁ASSURANCE
- ▁THIEF
- ▁SURVEY
- ▁VITAL
- ▁RAILWAY
- ▁JACKSON
- ▁CRASH
- ▁GROWL
- ▁COMBAT
- ▁RECOLLECTION
- ▁SECURITY
- ▁JACOB
- ▁CLUTCH
- ▁BLANKET
- ▁NANCY
- ▁CELLAR
- ▁CONVENIENT
- ▁INDIGNANT
- ▁COARSE
- ▁WORM
- ▁SCREEN
- ▁TRANSPORT
- ▁BULLET
- ▁APPRECIATE
- ▁DEVOTION
- ▁INVISIBLE
- ▁DRIED
- ▁MIXTURE
- ▁CANDID
- ▁PERFORMANCE
- ▁RIPE
- ▁EXQUISITE
- ▁BARGAIN
- ▁TOBACCO
- ▁LOYAL
- ▁MOULD
- ▁ATTENTIVE
- ▁DOROTHY
- ▁BRUTE
- ▁ESTABLISHMENT
- ▁ABILITY
- ▁INHABIT
- ▁OBSCURE
- ▁BORROW
- ▁ESSENCE
- ▁DISMAY
- ▁FLEE
- ▁BLADE
- ▁PLUCK
- ▁COFFIN
- ▁SUNSET
- ▁STEPHEN
- ▁ECONOMIC
- ▁HOLIDAY
- ▁MECHANICAL
- ▁COTTON
- ▁AWAKENED
- ▁SEIZE
- ▁RIDICULOUS
- ▁SANCHO
- ▁HESITATION
- ▁CORPSE
- ▁SAVING
- HOLD
- FOOT
- ▁ELDEST
- ▁DESPITE
- ▁EDITH
- ▁CHERISH
- ▁RESISTANCE
- ▁WILSON
- ▁ARGUE
- ▁INQUIRE
- ▁APPREHENSION
- ▁AVENUE
- ▁DRAKE
- ▁PROPOSE
- HURST
- ▁INFERIOR
- ▁STAIRCASE
- ▁WHEREFORE
- ▁CARLYLE
- ▁COUCH
- ▁ROUTE
- ▁POLITICS
- ▁TOMORROW
- ▁THRONG
- ▁NAUGHT
- ▁SUNLIGHT
- ▁INDIFFERENCE
- ▁OBEDIENCE
- ▁RECEPTION
- ▁VEGETABLE
- ▁IMPERFECT
- ▁RESIDENCE
- ▁TURKEY
- ▁VIOLET
- ▁SARAH
- ▁ALTAR
- ▁GRIEVE
- ▁JERK
- ▁ENSU
- ▁MAGICIAN
- ▁BLOSSOM
- ▁LANTERN
- ▁RESOLUTE
- ▁THOUGHTFULLY
- ▁FORTNIGHT
- ▁TRUMPET
- ▁VALJEAN
- ▁UNWILLING
- ▁LECTURE
- ▁WHEREUPON
- ▁HOLLAND
- ▁CHANGING
- ▁CREEK
- ▁SLICE
- ▁NORMAL
- ▁ANNIE
- ▁ACCENT
- ▁FREDERICK
- ▁DISAGREEABLE
- ▁RUBBED
- ▁DUMB
- ▁ESTABLISH
- ▁IMPORT
- ▁AFFIRM
- ▁MATTHEW
- ▁BRISK
- ▁CONVERT
- ▁BENDING
- ▁IVAN
- ▁MADEMOISELLE
- ▁MICHAEL
- ▁EASIER
- ▁JONES
- ▁FACING
- ▁EXCELLENCY
- ▁LITERARY
- ▁GOSSIP
- ▁DEVOUR
- ▁STAGGER
- ▁PENCIL
- ▁AVERAGE
- ▁HAMMER
- ▁TRIUMPHANT
- ▁PREFERRED
- ▁APPLICATION
- ▁OCCUPY
- ▁AUTHORITIES
- BURN
- ▁ASCERTAIN
- ▁CORRIDOR
- ▁DELICIOUS
- ▁PRACTISE
- ▁UNIVERSE
- ▁SHILLING
- ▁CONTEST
- ▁ASHORE
- ▁COMMIT
- ▁ADMINISTRATION
- ▁STUDIED
- ▁RIGID
- ▁ADORN
- ▁ELSEWHERE
- ▁INNOCENCE
- ▁JOURNAL
- ▁LANDSCAPE
- ▁TELEGRAPH
- ▁ANGRILY
- ▁CAMPAIGN
- ▁UNJUST
- ▁CHALLENGE
- ▁TORRENT
- ▁RELATE
- ▁ASSEMBLED
- ▁IMPRESSED
- ▁CANOE
- ▁CONCLUD
- ▁QUIXOTE
- ▁SATISFACTORY
- ▁NIECE
- ▁DEAF
- ▁RAFT
- ▁JIMMY
- ▁GLID
- ▁REGULAT
- ▁CHATTER
- ▁GLACIER
- ▁ENVY
- ▁STATUE
- ▁BOSTON
- ▁RICHMOND
- ▁DENIED
- ▁FANNY
- ▁SOLOMON
- ▁VULGAR
- ▁STALK
- ▁REPLACE
- ▁SPOON
- ▁BASIN
- ▁FEATURE
- ▁CONVICT
- ▁ARCHITECT
- ▁ADMIRAL
- ▁RIBBON
- ▁PERMANENT
- ▁APRIL
- ▁JOLLY
- ▁NEIGHBORHOOD
- ▁IMPART
- BOROUGH
- CAMP
- ▁HORRID
- ▁IMMORTAL
- ▁PRUDENCE
- ▁SPANIARD
- ▁SUPPOSING
- ▁TELEPHONE
- ▁TEMPERATURE
- ▁PENETRATE
- ▁OYSTER
- ▁APPOINTMENT
- ▁EGYPTIAN
- ▁DWELT
- ▁NEPHEW
- ▁RAILROAD
- ▁SEPTEMBER
- ▁DEVICE
- ▁WHEAT
- ▁GILBERT
- ▁ELEGANT
- ▁ADVERTISE
- ▁RATIONAL
- ▁TURTLE
- ▁BROOD
- ▁ASSEMBLY
- ▁CULTIVATE
- ▁EDITOR
- ▁SPECIMEN
- ▁UNDOUBTEDLY
- ▁WHALE
- ▁DROPPING
- ▁BALLOON
- ▁MEDICAL
- COMB
- ▁COMPOSITION
- ▁FOOTSTEPS
- ▁LAUNCELOT
- ▁DISCOURSE
- ▁ERRAND
- ▁CONVERSE
- ▁ADVANCING
- ▁DOWNSTAIRS
- ▁TUMULT
- ▁CORRUPT
- ▁SUFFICE
- ▁ANGUISH
- ▁SHAGGY
- ▁RETIRE
- ▁TIMBER
- ▁BLAZE
- ▁ABSTRACT
- ▁EMBROIDER
- ▁PHOTOGRAPH
- ▁PROSPERITY
- ▁TERRIBLY
- ▁TERRITORY
- ▁THRESHOLD
- ▁PAVEMENT
- ▁INJURED
- ▁LIMP
- ▁AGITATION
- ▁RASCAL
- ▁PRESUME
- ▁OBSERVING
- ▁OBSTACLE
- ▁SIMPLICITY
- ▁SLUMBER
- ▁SUPPLIED
- ▁COMBINATION
- ▁DRAIN
- ▁WILDERNESS
- ▁BELIEVING
- ▁VILLAIN
- ▁RECKLESS
- ▁INJURY
- ▁CLAPP
- ▁FRIDAY
- ▁HERCULES
- ▁KENNEDY
- ▁SYMPTOM
- ▁SLEDGE
- ▁CEILING
- ▁LEMON
- ▁PLAGUE
- ▁MONDAY
- ▁CANVAS
- ▁IMPATIENCE
- ▁UNCOMFORTABLE
- ▁ACCESS
- ▁FROZEN
- ▁SENATOR
- ▁FRANZ
- ▁SWIMMING
- ▁BARRIER
- ▁ADJUST
- ▁COMPARISON
- ▁PROCLAIM
- ▁WRINKL
- ▁OVERLOOK
- ▁MITYA
- ▁GUILT
- ▁PERCEPTION
- ▁PRECAUTION
- ▁SPECTATOR
- ▁SURPRISING
- ▁DISTRACT
- ▁DISDAIN
- ▁BONNET
- ▁MAGNET
- ▁PROFESS
- ▁CONFOUND
- ▁NARRATIVE
- ▁STRUCTURE
- ▁SKETCH
- ▁ULTIMATE
- ▁GLOBE
- ▁INSECT
- FICIENCY
- ▁ORCHARD
- ▁AMIABLE
- ▁DESCENT
- ▁INDEPENDENCE
- ▁MANUFACTURE
- ▁SPRINKLE
- ▁NIGHTINGALE
- ▁CUSHION
- ▁EMINENT
- ▁SCOTT
- ▁ARRAY
- ▁COSETTE
- ▁WAVING
- ▁EXTRACT
- ▁IRREGULAR
- ▁PERSECUT
- ▁DERIVED
- ▁WITHDREW
- ▁CAUTION
- ▁SUSPICIOUS
- ▁MEMORIES
- ▁NOWHERE
- ▁SUBTLE
- ▁THOROUGH
- Q
- ▁APPROPRIATE
- ▁SLAUGHTER
- ▁YOURSELVES
- ▁THUMB
- ▁TWAS
- ▁ABODE
- ▁BIDDING
- ▁CONSPICUOUS
- ▁REBECCA
- ▁SERGEANT
- ▁APRON
- ▁ANTICIPATE
- ▁DISCIPLINE
- ▁GLANCING
- ▁PILGRIM
- ▁SULLEN
- ▁CONTRIBUTE
- ▁PRAIRIE
- ▁CARVED
- ▁COMMERCE
- ▁EXCLAMATION
- ▁MUSCULAR
- ▁NOVEMBER
- ▁PHENOMENA
- ▁SYMBOL
- ▁UMBRELLA
- ▁DIMINISH
- ▁PARLOUR
- ▁THREATENING
- ▁STUMP
- ▁EXTENSIVE
- ▁PLEASING
- ▁REMEMBRANCE
- ▁COMBINED
- ▁SHERIFF
- ▁SHAFT
- ▁LAURA
- ▁INTERCOURSE
- ▁STRICKEN
- ▁SUPPLIES
- ▁LANDLORD
- ▁SHRINK
- ▁PRICK
- ▁CAESAR
- ▁DRUG
- ▁BEWILDERED
- ▁NAUTILUS
- ▁BRUTAL
- ▁COMMERCIAL
- ▁MAGGIE
- ▁SPHERE
- ▁VIRGIN
- ▁BRETHREN
- ▁DESTINY
- ▁POLICY
- ▁TERRIFIED
- ▁HOUSEKEEPER
- ▁CRAZY
- ▁ARDENT
- ▁DISCERN
- ▁WRAP
- ▁MARQUIS
- ▁RUSSIA
- MOUTH
- ▁BRITAIN
- ▁HARBOUR
- ▁CONCERT
- ▁DONKEY
- ▁DAMAGE
- ▁SLIM
- ABOUT
- ▁LUXURY
- ▁MONSTROUS
- ▁TENDENCY
- ▁PARADISE
- ▁CULTURE
- ▁JULIUS
- ▁RAOUL
- ▁REMEDY
- ▁DECAY
- ▁SCOLD
- ▁SPLIT
- ▁ASSAULT
- ▁DECEMBER
- ▁MOSCOW
- ▁EXPLORE
- ▁TROUSERS
- ▁WRIST
- PIECE
- ▁MUSKET
- ▁VALENTINE
- ▁TYRANT
- ▁ABRAHAM
- ▁MEDIUM
- ▁ARTIFICIAL
- ▁FACULTY
- ▁OBLIGATION
- ▁RESEMBLANCE
- ▁INQUIRIES
- ▁DETAIN
- ▁SWARM
- ▁PLEDGE
- ▁ADMIRABLE
- ▁DEFECT
- ▁SUPERINTEND
- ▁PATRIOT
- ▁CLUNG
- ▁DISMAL
- ▁RECIT
- ▁IGNOR
- ▁AMELIA
- ▁JUSTIFY
- ▁ELEPHANT
- ▁ESTIMATE
- ▁KNELT
- ▁SERVING
- ▁WHIM
- ▁SHRILL
- ▁STUDIO
- ▁TEXT
- ▁ALEXANDER
- ▁WROUGHT
- ▁ABUNDANT
- ▁SITUATED
- ▁REGAIN
- ▁FIERY
- ▁SNEER
- ▁SWEAT
- ▁GLARE
- ▁NIGH
- ▁ESCORT
- ▁INEVITABLE
- ▁PSMITH
- ▁RELUCTANT
- ▁PRECEDING
- ▁RESORT
- ▁OUTRAGE
- ▁AMBASSADOR
- ▁CONSOLATION
- ▁RECOGNITION
- ▁REMORSE
- ▁BEHALF
- ▁FORMIDABLE
- ▁GRAVITY
- ▁DIVIDE
- ▁CONFRONT
- ▁GIGANTIC
- ▁OCTOBER
- ▁FLANK
- ▁SLEW
- ▁CLARA
- ▁FILM
- ▁BULK
- ▁POMP
- ▁ELEANOR
- ▁EMPHASIS
- ▁JAPANESE
- ▁CAVALRY
- ▁EXCLUSIVE
- ▁PERFUME
- ▁BRONZE
- ▁FEDERAL
- ▁LIQUID
- ▁RUBBING
- ▁OVEN
- DOLPH
- ▁CONVULS
- ▁DEPRIVED
- ▁RESPONSIBILITY
- ▁SIGNIFICANT
- ▁WAISTCOAT
- ▁CLUSTER
- ▁MARTHA
- ▁REVERSE
- ▁ATTORNEY
- ▁DROOP
- ▁SKILFUL
- ▁HABITUAL
- ▁PUMP
- ▁INTERVEN
- ▁OWL
- ▁CONJECTURE
- ▁FANTASTIC
- ▁RESPONSIBLE
- ▁DESTINED
- ▁DOCUMENT
- ▁THEREUPON
- ▁GODDESS
- ▁PACIFIC
- ▁WARRANT
- ▁COSTUME
- ▁BRIDLE
- ▁CALIFORNIA
- ▁DEMOCRATIC
- ▁EUSTACE
- ▁SQUIRREL
- ▁UNCOMMON
- ▁MARVELLOUS
- ▁PLOUGH
- ▁TRAGEDY
- ▁VAULT
- ▁HESITATE
- ▁REFRAIN
- ▁ADMIRING
- ▁CORPORAL
- ▁ENTITLED
- ▁SHREWD
- ▁SQUEEZ
- ▁ACCURATE
- ▁TEMPEST
- ▁MONUMENT
- ▁SIEGE
- ▁CHINESE
- ▁RAVEN
- ▁LOUNG
- ▁ASSASSIN
- ▁INFLICT
- ▁AGITATED
- ▁DESIRABLE
- ▁EARLIEST
- ▁LAUNCH
- ▁PILOT
- ▁PULSE
- ▁MUTE
- LEIGH
- ▁LIQUOR
- ▁SCARECROW
- ▁SKULL
- ▁DESOLATE
- ▁SUBLIME
- ▁SERENE
- ▁RECESS
- ▁WAKING
- ▁CHARLOTTE
- ▁CIRCULAR
- ▁INJUSTICE
- ▁PINOCCHIO
- ▁PRISCILLA
- ▁THYSELF
- ▁OCCURRENCE
- ▁CASUAL
- ▁FRANTIC
- ▁LEGEND
- ▁FERTIL
- ▁BACKGROUND
- ▁DELICACY
- ▁ESTRALLA
- ▁MANUSCRIPT
- ▁RESPONSE
- ▁UNIVERSITY
- ▁WOLVES
- ▁SCANDAL
- ▁STUMBLE
- ▁HOARSE
- ▁BODILY
- ▁CONVENT
- ▁EXAMINING
- ▁INCAPABLE
- ▁PERCEIVING
- ▁PHILADELPHIA
- ▁SUBSEQUENT
- ▁THIEVES
- ▁ACCUMULAT
- ▁DAMSEL
- ▁SCOTCH
- ▁UNDERNEATH
- ▁NOBILITY
- ▁SMASH
- ▁REVOLT
- ▁ENGAGE
- ▁CATHEDRAL
- ▁CHAMPION
- ▁DESPATCH
- ▁ETERNITY
- ▁JANUARY
- ▁PLEADED
- ▁PROBABILITY
- ▁JIMMIE
- ▁PARALLEL
- ▁FISHERMAN
- ▁JERRY
- ▁SWORE
- ▁DRAUGHT
- ▁OPPONENT
- ▁PRIMITIVE
- ▁SIGNIFICANCE
- ▁SUBSTANTIAL
- ▁AMAZED
- ▁DUNBAR
- ▁COMMEND
- ▁CONTEMPLATE
- ▁TESTIMONY
- ▁IMPERIAL
- ▁ADAPT
- ▁JUICE
- ▁CALAMIT
- CULAR
- ▁CHATEAU
- ▁PHOENIX
- ▁PRUDENT
- ▁SOLUTION
- ▁VILLEFORT
- ▁REACTION
- ▁RELAX
- ▁YU
- ▁PROHIBIT
- ▁DISTRUST
- ▁PLUNDER
- ▁WELFARE
- ▁NAVIGAT
- ▁PARLOR
- ▁LAZY
- ▁DETACH
- OMETER
- ▁PRIV
- ▁DISCOURAGE
- ▁OBSTINATE
- ▁REJOICING
- ▁SERMON
- ▁VEHICLE
- ▁FANCIES
- ▁ENLIGHTEN
- ▁ACUTE
- ▁ILLUSION
- ▁ANTHEA
- ▁MARTIAN
- ▁EXCITE
- ▁GENEROSITY
- OLOGIST
- ▁AMAZING
- ▁UNWORTHY
- ▁INTERNAL
- ▁INCENSE
- ▁VIBRAT
- ▁ADHERE
- ROACH
- ▁FEBRUARY
- ▁MEXICAN
- ▁POTATOES
- ▁INCESSANT
- ▁INTERPOSED
- ▁PARCEL
- ▁VEXED
- ▁PROMOTE
- MIDST
- ▁ARISTOCRAT
- ▁CYRIL
- ▁EMBARK
- ▁ABUNDANCE
- ▁LITERALLY
- ▁SURGEON
- ▁TERRACE
- ▁ATLANTIC
- ▁MARTYR
- ▁SPECK
- ▁SENATE
- ▁LOAF
- ▁ADMINISTER
- ▁APPREHEND
- ▁SUBDUED
- ▁TEMPORARY
- ▁DOMINION
- ▁ELABORATE
- ▁DIGNIFIED
- ▁ELIZA
- ▁SPLASH
- ▁CONSEIL
- ▁DEXTER
- ▁UNSEEN
- ▁TRAGIC
- VOCATION
- ▁GRATIFY
- ▁BACHELOR
- ▁DEFENSE
- ▁EXCURSION
- ▁FACULTIES
- ▁PROPRIETOR
- ▁SYMPATHETIC
- ▁UNNECESSARY
- ▁RADIANT
- ▁VACANT
- ▁OUNCE
- ▁SCREW
- ▁PHENOMENON
- ▁PROMINENT
- ▁WORRIED
- ▁STUDIES
- ▁CLIMATE
- ▁KEITH
- ▁ARAMIS
- ▁BLISS
- ▁CONTINUAL
- ▁SURPASS
- ▁HEBREW
- ▁IDENTITY
- ▁PROVOKE
- ▁TEMPERAMENT
- ▁CHARIOT
- ▁HARBOR
- ▁NINTH
- ▁PRIOR
- ▁DESIROUS
- ▁JERUSALEM
- ▁UNDERTAKING
- ▁EDISON
- ▁MIRTH
- ▁SCOUT
- ▁APPARATUS
- ▁ILLUSTRATION
- ▁INTELLIGIBLE
- ▁INVARIABLY
- ▁PIERCED
- ▁REVIEW
- ▁FLICKER
- ▁HAZARD
- ▁REVELATION
- ▁DIXON
- ▁EXCITING
- ▁GOSPEL
- ▁CONSTANCE
- ▁OVERTAKE
- ▁GUINEA
- ▁ALADDIN
- ▁CHICAGO
- ▁TULLIVER
- ▁HAMILTON
- ▁GARRISON
- ▁DISCIPLE
- ▁INTENSITY
- ▁TRAITOR
- ▁CHANCELLOR
- ▁PROVERB
- ▁DAGGER
- ▁FORESEE
- ▁CONFIDE
- ▁GLIMMER
- ▁CHAUVELIN
- ▁ILLUSTRATE
- ▁VOLUNTEER
- ▁JUNGLE
- ▁STREAK
- ▁SUNRISE
- ▁DISSOLV
- ▁QUEST
- ▁AWHILE
- ▁FELICITY
- ▁LEGISLATURE
- ▁LEONORA
- ▁MAGAZINE
- ▁PITIFUL
- ▁COLONY
- ▁SHAWL
- ▁ARRIVING
- ▁FUNDAMENTAL
- ▁CARPENTER
- ▁OVERFLOW
- ▁EXPAND
- ▁HARVEST
- ▁FEMININE
- ▁INNUMERABLE
- ▁SCRAMBLE
- ▁TWENTIETH
- ▁TRIFLING
- ▁GHASTL
- ▁CONQUEST
- ▁DANIEL
- ▁FACILIT
- ▁FORSAKE
- ▁BEHAVIOUR
- ▁GORGEOUS
- ▁PRODUCING
- ▁HAPPIER
- ▁PROMISING
- ▁RAINBOW
- ▁INSTINCTIVELY
- ▁DECREE
- ▁EYEBROWS
- ▁IRRESISTIBLE
- ▁PHARAOH
- ▁SCROOGE
- ▁UNNATURAL
- ▁CRUMBS
- ▁REFINED
- ▁DREARY
- ▁TRENCH
- ▁CONVINCE
- ▁FRINGE
- ▁EXTREMITY
- ▁INTIMACY
- ▁SCOUNDREL
- ▁SUFFRAGE
- ▁UNEASINESS
- ▁BARRICADE
- ▁CIRCULAT
- ▁SAMUEL
- ▁BRUCE
- ▁DARCY
- <sos/eos>
init: xavier_uniform
input_size: 83
ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: false
model_conf:
    ctc_weight: 0.3
    lsm_weight: 0.1
    length_normalized_loss: false
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram5000/bpe.model
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: null
frontend_conf: {}
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: global_mvn
normalize_conf:
    stats_file: exp/asr_stats_fbank_pitch_en_bpe5000_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: contextual_block_transformer
encoder_conf:
    output_size: 256
    attention_heads: 4
    linear_units: 2048
    num_blocks: 12
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    attention_dropout_rate: 0.0
    input_layer: conv2d
    normalize_before: true
    block_size: 40
    hop_size: 16
    look_ahead: 16
    init_average: true
    ctx_pos_enc: true
decoder: transformer
decoder_conf:
    attention_heads: 4
    linear_units: 2048
    num_blocks: 6
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    self_attention_dropout_rate: 0.0
    src_attention_dropout_rate: 0.0
required:
- output_dir
- token_list
version: 0.9.7
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}
}
```


---
license: mit
datasets:
- tweet_eval
language: en
widget:
- text: "You're a complete idiot!"
  example_title: "Offensive"
- text: "I am tired of studying for tomorrow's exam"
  example_title: "Non-Offensive"
---


---
license: mit
datasets:
- tweet_eval
language: en
widget:
- text: "Stop sharing which songs did you listen to during this year on Spotify, NOBODY CARES"
  example_title: "Anger"
- text: "I love that joke HAHAHAHAHA"
  example_title: "Joy"
- text: "Despite I've not studied a lot for this exam, I think I will pass  😜"
  example_title: "Optimism"
- text: "My dog died this morning..."
  example_title: "Sadness"
---


---
license: mit
datasets:
- tweet_eval
language: en
widget:
- text: "I love summer!"
  example_title: "Positive"
- text: "Does anyone want to play?"
  example_title: "Neutral"
- text: "This movie is just awful! 😫"
  example_title: "Negative"
---


---
language:
- en
thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg
tags:
- question-answering
license: apache-2.0
datasets:
- squad
metrics:
- squad
---

# DistilBERT with a second step of distillation

## Model description

This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation.

In this version, the following pre-trained models were used:

* Student: `distilbert-base-uncased`
* Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1`

## Training data

This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows:

```python
from datasets import load_dataset
squad = load_dataset('squad')
```

## Training procedure

## Eval results

|                  | Exact Match | F1   |
|------------------|-------------|------|
| DistilBERT paper | 79.1        | 86.9 |
| Ours             | 78.4        | 86.5 |

The scores were calculated using the `squad` metric from `datasets`.

### BibTeX entry and citation info

```bibtex
@misc{sanh2020distilbert,
      title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
      author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
      year={2020},
      eprint={1910.01108},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


---
language: id
tags:
- roberta
license: mit
datasets:
- indonli
widget:
- text: "Amir Sjarifoeddin Harahap lahir di Kota Medan, Sumatera Utara, 27 April 1907. Ia meninggal di Surakarta, Jawa Tengah, pada 19 Desember 1948 dalam usia 41 tahun. </s></s> Amir Sjarifoeddin Harahap masih hidup."
---

## Indo-roberta-indonli

Indo-roberta-indonli is natural language inference classifier based on [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) model. It was trained on the trained on [IndoNLI](https://github.com/ir-nlp-csui/indonli/tree/main/data/indonli) dataset. The model used was [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) and was transfer-learned to a natural inference classifier model. The model are tested using the validation, test_layer and test_expert dataset given in the github repository. The results are shown below.

### Result
| Dataset     | Accuracy | F1      | Precision | Recall  |
|-------------|----------|---------|-----------|---------|
|    Test Lay | 0.74329  | 0.74075 |   0.74283 | 0.74133 |
| Test Expert | 0.6115   | 0.60543 |   0.63924 | 0.61742 |

## Model

The model was trained on with 5 epochs, batch size 16, learning rate 2e-5 and weight decay 0.01. Achieved different metrics as shown below.

| Epoch | Training Loss | Validation Loss | Accuracy | F1       | Precision | Recall   |
|-------|---------------|-----------------|----------|----------|-----------|----------|
|     1 |      0.942500 |        0.658559 | 0.737369 | 0.735552 |  0.735488 | 0.736679 |
|     2 |      0.649200 |        0.645290 | 0.761493 | 0.759593 |  0.762784 | 0.759642 |
|     3 |      0.437100 |        0.667163 | 0.766045 | 0.763979 |  0.765740 | 0.763792 |
|     4 |      0.282000 |        0.786683 | 0.764679 | 0.761802 |  0.762011 | 0.761684 |
|     5 |      0.193500 |        0.925717 | 0.765134 | 0.763127 |  0.763560 | 0.763489 |

## How to Use
### As NLI Classifier
```python
from transformers import pipeline
pretrained_name = "StevenLimcorn/indonesian-roberta-indonli"
nlp = pipeline(
    "zero-shot-classification",
    model=pretrained_name,
    tokenizer=pretrained_name
)
nlp("Amir Sjarifoeddin Harahap lahir di Kota Medan, Sumatera Utara, 27 April 1907. Ia meninggal di Surakarta, Jawa Tengah, pada 19 Desember 1948 dalam usia 41 tahun. </s></s> Amir Sjarifoeddin Harahap masih hidup.")
```
## Disclaimer
Do consider the biases which come from both the pre-trained RoBERTa model and the `INDONLI` dataset that may be carried over into the results of this model.
## Author
Indonesian RoBERTa Base IndoNLI was trained and evaluated by [Steven Limcorn](https://github.com/stevenlimcorn). All computation and development are done on Google Colaboratory using their free GPU access.

## Reference
The dataset we used is by IndoNLI.
```
@inproceedings{indonli,
    title = "IndoNLI: A Natural Language Inference Dataset for Indonesian",
    author = "Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    publisher = "Association for Computational Linguistics",
}
```


---
language: ko
license: apache-2.0
tags:
  - automatic-speech-recognition
  - robust-speech-event
  - generated_from_trainer
datasets:
  - kresnik/zeroth_korean
model-index:
- name: 'Wav2Vec2 XLS-R Korean 1B'
  results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Zeroth Korean
        type: kresnik/zeroth_korean
        args: clean
      metrics:
        - name: Test WER
          type: wer
          value: 8.62
        - name: Test CER
          type: cer
          value: 2.24
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: ko
      metrics:
        - name: Test WER
          type: wer
          value: 88.402
        - name: Test CER
          type: cer
          value: 48.74
---

<!-- 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 is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0960
- Wer: 0.0862
- Cer: 0.0224

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 3.765         | 0.72  | 500   | 2.5988          | 0.9742 | 0.5570 |
| 2.4829        | 1.44  | 1000  | 1.2169          | 0.7622 | 0.2989 |
| 2.522         | 2.16  | 1500  | 1.1796          | 0.7810 | 0.3164 |
| 2.5122        | 2.88  | 2000  | 1.1773          | 0.8269 | 0.3537 |
| 2.4101        | 3.6   | 2500  | 1.1751          | 0.7762 | 0.3263 |
| 2.3391        | 4.32  | 3000  | 1.0176          | 0.7320 | 0.2853 |
| 2.2714        | 5.04  | 3500  | 0.9985          | 0.7351 | 0.2883 |
| 2.1833        | 5.75  | 4000  | 0.8875          | 0.7005 | 0.2598 |
| 2.1531        | 6.47  | 4500  | 0.9157          | 0.6907 | 0.2628 |
| 2.1019        | 7.19  | 5000  | 0.9093          | 0.7043 | 0.2626 |
| 2.0943        | 7.91  | 5500  | 0.8949          | 0.7095 | 0.2635 |
| 2.0285        | 8.63  | 6000  | 0.8583          | 0.6894 | 0.2551 |
| 1.9057        | 9.35  | 6500  | 0.7278          | 0.6176 | 0.2094 |
| 1.8583        | 10.07 | 7000  | 0.8197          | 0.6605 | 0.2401 |
| 1.8095        | 10.79 | 7500  | 0.6592          | 0.5983 | 0.1971 |
| 1.7588        | 11.51 | 8000  | 0.6004          | 0.5572 | 0.1767 |
| 1.702         | 12.23 | 8500  | 0.6145          | 0.5580 | 0.1795 |
| 1.6375        | 12.95 | 9000  | 0.5200          | 0.5364 | 0.1628 |
| 1.6138        | 13.67 | 9500  | 0.5244          | 0.5186 | 0.1590 |
| 1.5441        | 14.39 | 10000 | 0.5044          | 0.5211 | 0.1552 |
| 1.4886        | 15.11 | 10500 | 0.4284          | 0.4585 | 0.1328 |
| 1.4136        | 15.83 | 11000 | 0.4393          | 0.4644 | 0.1351 |
| 1.39          | 16.55 | 11500 | 0.4154          | 0.4410 | 0.1273 |
| 1.3509        | 17.27 | 12000 | 0.3821          | 0.4169 | 0.1171 |
| 1.3161        | 17.98 | 12500 | 0.3506          | 0.4091 | 0.1086 |
| 1.259         | 18.7  | 13000 | 0.3359          | 0.3819 | 0.1031 |
| 1.2426        | 19.42 | 13500 | 0.3289          | 0.3744 | 0.1010 |
| 1.197         | 20.14 | 14000 | 0.3267          | 0.3688 | 0.0977 |
| 1.1884        | 20.86 | 14500 | 0.3111          | 0.3555 | 0.0931 |
| 1.1479        | 21.58 | 15000 | 0.3059          | 0.3479 | 0.0931 |
| 1.0864        | 22.3  | 15500 | 0.2783          | 0.3300 | 0.0849 |
| 1.0763        | 23.02 | 16000 | 0.2566          | 0.2937 | 0.0752 |
| 1.0521        | 23.74 | 16500 | 0.2568          | 0.2986 | 0.0771 |
| 1.012         | 24.46 | 17000 | 0.2448          | 0.2921 | 0.0738 |
| 0.9831        | 25.18 | 17500 | 0.2330          | 0.2753 | 0.0682 |
| 0.9575        | 25.9  | 18000 | 0.2231          | 0.2723 | 0.0675 |
| 0.9325        | 26.62 | 18500 | 0.2307          | 0.2857 | 0.0720 |
| 0.8867        | 27.34 | 19000 | 0.2031          | 0.2446 | 0.0603 |
| 0.8793        | 28.06 | 19500 | 0.2095          | 0.2481 | 0.0615 |
| 0.8382        | 28.78 | 20000 | 0.1893          | 0.2199 | 0.0538 |
| 0.79          | 29.5  | 20500 | 0.1800          | 0.2122 | 0.0509 |
| 0.7761        | 30.22 | 21000 | 0.1719          | 0.2064 | 0.0489 |
| 0.7911        | 30.93 | 21500 | 0.1707          | 0.1950 | 0.0485 |
| 0.7413        | 31.65 | 22000 | 0.1631          | 0.1915 | 0.0464 |
| 0.7186        | 32.37 | 22500 | 0.1583          | 0.1753 | 0.0426 |
| 0.712         | 33.09 | 23000 | 0.1477          | 0.1752 | 0.0427 |
| 0.693         | 33.81 | 23500 | 0.1522          | 0.1725 | 0.0419 |
| 0.6692        | 34.53 | 24000 | 0.1549          | 0.1702 | 0.0408 |
| 0.6409        | 35.25 | 24500 | 0.1439          | 0.1548 | 0.0378 |
| 0.6464        | 35.97 | 25000 | 0.1422          | 0.1534 | 0.0373 |
| 0.6192        | 36.69 | 25500 | 0.1375          | 0.1489 | 0.0364 |
| 0.5924        | 37.41 | 26000 | 0.1278          | 0.1367 | 0.0330 |
| 0.5757        | 38.13 | 26500 | 0.1269          | 0.1278 | 0.0324 |
| 0.565         | 38.85 | 27000 | 0.1242          | 0.1306 | 0.0319 |
| 0.5326        | 39.57 | 27500 | 0.1228          | 0.1373 | 0.0338 |
| 0.5218        | 40.29 | 28000 | 0.1251          | 0.1326 | 0.0324 |
| 0.5248        | 41.01 | 28500 | 0.1205          | 0.1184 | 0.0294 |
| 0.5027        | 41.73 | 29000 | 0.1128          | 0.1087 | 0.0276 |
| 0.4775        | 42.45 | 29500 | 0.1117          | 0.1071 | 0.0270 |
| 0.4765        | 43.17 | 30000 | 0.1055          | 0.1036 | 0.0265 |
| 0.4626        | 43.88 | 30500 | 0.1046          | 0.1045 | 0.0268 |
| 0.4411        | 44.6  | 31000 | 0.1035          | 0.1045 | 0.0270 |
| 0.4351        | 45.32 | 31500 | 0.0996          | 0.0929 | 0.0244 |
| 0.4358        | 46.04 | 32000 | 0.1017          | 0.0947 | 0.0241 |
| 0.4185        | 46.76 | 32500 | 0.0982          | 0.0903 | 0.0235 |
| 0.4178        | 47.48 | 33000 | 0.0983          | 0.0892 | 0.0230 |
| 0.4038        | 48.2  | 33500 | 0.0981          | 0.0895 | 0.0230 |
| 0.3876        | 48.92 | 34000 | 0.0968          | 0.0888 | 0.0225 |
| 0.3879        | 49.64 | 34500 | 0.0958          | 0.0862 | 0.0221 |

### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0


---
language:
- zh-TW
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
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 is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - ZH-TW dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1786
- Wer: 0.8594
- Cer: 0.2964

## 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: 7.5e-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
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 64.6189       | 2.51  | 500   | 63.8077         | 1.0    | 1.0    |
| 8.0561        | 5.03  | 1000  | 6.8014          | 1.0    | 1.0    |
| 6.0427        | 7.54  | 1500  | 6.0745          | 1.0    | 1.0    |
| 5.9357        | 10.05 | 2000  | 5.8682          | 1.0    | 1.0    |
| 5.0489        | 12.56 | 2500  | 4.4032          | 0.9990 | 0.7750 |
| 4.6184        | 15.08 | 3000  | 3.8383          | 0.9983 | 0.6768 |
| 4.365         | 17.59 | 3500  | 3.4633          | 0.9959 | 0.6299 |
| 4.1026        | 20.1  | 4000  | 3.0732          | 0.9902 | 0.5814 |
| 3.8655        | 22.61 | 4500  | 2.7638          | 0.9868 | 0.5465 |
| 3.6991        | 25.13 | 5000  | 2.4759          | 0.9811 | 0.5088 |
| 3.4894        | 27.64 | 5500  | 2.2937          | 0.9746 | 0.4852 |
| 3.3983        | 30.15 | 6000  | 2.1684          | 0.9733 | 0.4674 |
| 3.2736        | 32.66 | 6500  | 2.0372          | 0.9659 | 0.4458 |
| 3.1884        | 35.18 | 7000  | 1.9267          | 0.9648 | 0.4329 |
| 3.1248        | 37.69 | 7500  | 1.8408          | 0.9591 | 0.4217 |
| 3.0381        | 40.2  | 8000  | 1.7531          | 0.9503 | 0.4074 |
| 2.9515        | 42.71 | 8500  | 1.6880          | 0.9459 | 0.3967 |
| 2.8704        | 45.23 | 9000  | 1.6264          | 0.9378 | 0.3884 |
| 2.8128        | 47.74 | 9500  | 1.5621          | 0.9341 | 0.3782 |
| 2.7386        | 50.25 | 10000 | 1.5011          | 0.9243 | 0.3664 |
| 2.6646        | 52.76 | 10500 | 1.4608          | 0.9192 | 0.3575 |
| 2.6072        | 55.28 | 11000 | 1.4251          | 0.9148 | 0.3501 |
| 2.569         | 57.79 | 11500 | 1.3837          | 0.9060 | 0.3462 |
| 2.5091        | 60.3  | 12000 | 1.3589          | 0.9070 | 0.3392 |
| 2.4588        | 62.81 | 12500 | 1.3261          | 0.8966 | 0.3284 |
| 2.4083        | 65.33 | 13000 | 1.3052          | 0.8982 | 0.3265 |
| 2.3787        | 67.84 | 13500 | 1.2997          | 0.8908 | 0.3243 |
| 2.3457        | 70.35 | 14000 | 1.2778          | 0.8898 | 0.3187 |
| 2.3099        | 72.86 | 14500 | 1.2661          | 0.8830 | 0.3172 |
| 2.2559        | 75.38 | 15000 | 1.2475          | 0.8851 | 0.3143 |
| 2.2264        | 77.89 | 15500 | 1.2319          | 0.8739 | 0.3085 |
| 2.196         | 80.4  | 16000 | 1.2218          | 0.8722 | 0.3049 |
| 2.1613        | 82.91 | 16500 | 1.2093          | 0.8719 | 0.3051 |
| 2.1455        | 85.43 | 17000 | 1.2055          | 0.8624 | 0.3005 |
| 2.1193        | 87.94 | 17500 | 1.1975          | 0.8600 | 0.2982 |
| 2.0911        | 90.45 | 18000 | 1.1960          | 0.8648 | 0.3003 |
| 2.0884        | 92.96 | 18500 | 1.1871          | 0.8638 | 0.2971 |
| 2.0766        | 95.48 | 19000 | 1.1814          | 0.8617 | 0.2967 |
| 2.0735        | 97.99 | 19500 | 1.1801          | 0.8621 | 0.2969 |

### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0


---
language: id
tags:
- roberta
license: mit
datasets:
- indonlu
widget:
- text: "Hal-hal baik akan datang."
---

# Indo RoBERTa Emotion Classifier

Indo RoBERTa Emotion Classifier is emotion classifier based on [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) model. It was trained on the trained on [IndoNLU EmoT](https://huggingface.co/datasets/indonlu) dataset. The model used was [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) and was transfer-learned to an emotion classifier model. Based from the [IndoNLU bencmark](https://www.indobenchmark.com/), the model achieve an f1-macro of 72.05%, accuracy of 71.81%, precision of 72.47% and recall of 71.94%.

## Model

The model was trained on 7 epochs with learning rate 2e-5. Achieved different metrics as shown below.

| Epoch | Training Loss | Validation Loss | Accuracy | F1       | Precision | Recall   |
|-------|---------------|-----------------|----------|----------|-----------|----------|
|     1 |      1.300700 |        1.005149 | 0.622727 | 0.601846 |  0.640845 | 0.611144 |
|     2 |      0.806300 |        0.841953 | 0.686364 | 0.694096 |  0.701984 | 0.696657 |
|     3 |      0.591900 |        0.796794 | 0.686364 | 0.696573 |  0.707520 | 0.691671 |
|     4 |      0.441200 |        0.782094 | 0.722727 | 0.724359 |  0.725985 | 0.730229 |
|     5 |      0.334700 |        0.809931 | 0.711364 | 0.720550 |  0.718318 | 0.724608 |
|     6 |      0.268400 |        0.812771 | 0.718182 | 0.724192 |  0.721222 | 0.729195 |
|     7 |      0.226000 |        0.828461 | 0.725000 | 0.733625 |  0.731709 | 0.735800 |

## How to Use
### As Text Classifier
```python
from transformers import pipeline
pretrained_name = "StevenLimcorn/indonesian-roberta-base-emotion-classifier"
nlp = pipeline(
    "sentiment-analysis",
    model=pretrained_name,
    tokenizer=pretrained_name
)
nlp("Hal-hal baik akan datang.")
```
## Disclaimer
Do consider the biases which come from both the pre-trained RoBERTa model and the `EmoT` dataset that may be carried over into the results of this model.
## Author
Indonesian RoBERTa Base Emotion Classifier was trained and evaluated by [Steven Limcorn](https://github.com/stevenlimcorn). All computation and development are done on Google Colaboratory using their free GPU access.


---
language: ms
tags:
- melayu-bert
license: mit
datasets:
- oscar
widget:
- text: "Saya [MASK] makan nasi hari ini."
---

## Melayu BERT

Melayu BERT is a masked language model based on [BERT](https://arxiv.org/abs/1810.04805). It was trained on the [OSCAR](https://huggingface.co/datasets/oscar) dataset, specifically the `unshuffled_original_ms` subset. The model used was [English BERT model](https://huggingface.co/bert-base-uncased) and fine-tuned on the Malaysian dataset. The model achieved a perplexity of 9.46 on a 20% validation dataset. Many of the techniques used are based on a Hugging Face tutorial [notebook](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling.ipynb) written by [Sylvain Gugger](https://github.com/sgugger), and [fine-tuning tutorial notebook](https://github.com/piegu/fastai-projects/blob/master/finetuning-English-GPT2-any-language-Portuguese-HuggingFace-fastaiv2.ipynb) written by [Pierre Guillou](https://huggingface.co/pierreguillou). The model is available both for PyTorch and TensorFlow use.

## Model

The model was trained on 3 epochs with a learning rate of 2e-3 and achieved a training loss per steps as shown below.

|  Step  |Training loss|
|--------|-------------|
|500	 |  5.051300   |
|1000	 |  3.701700   |
|1500	 |  3.288600   |
|2000	 |  3.024000   |
|2500	 |  2.833500   |
|3000	 |  2.741600   |
|3500	 |  2.637900   |
|4000	 |  2.547900   |
|4500	 |  2.451500   |
|5000	 |  2.409600   |
|5500	 |  2.388300   |
|6000	 |  2.351600   |

## How to Use
### As Masked Language Model
```python
from transformers import pipeline
pretrained_name = "StevenLimcorn/MelayuBERT"
fill_mask = pipeline(
    "fill-mask",
    model=pretrained_name,
    tokenizer=pretrained_name
)
fill_mask("Saya [MASK] makan nasi hari ini.")
```

### Import Tokenizer and Model
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("StevenLimcorn/MelayuBERT")

model = AutoModelForMaskedLM.from_pretrained("StevenLimcorn/MelayuBERT")
```
## Author
Melayu BERT was trained by [Steven Limcorn](https://github.com/stevenlimcorn) and [Wilson Wongso](https://hf.co/w11wo).


---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-finetuned-scientific-articles
  results: []
---

This repository is the submission for the final project for BF510 [Institutional Racism in Health and Science](http://irhs.bu.edu/) for Shariq Madha.

To see Jupyter detailing how this model was produced, as well as the motivation behind it, go [here](https://github.com/ssmadha/BF510-final-project/).

To try this out yourself, enter a prompt in the textbox to the right and hit compute (it may take a minute for the first to process, but subsequent results should be quick).

# gpt2-finetuned-scientific-articles

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on scientific articles about algorithmic bias.
It achieves the following results on the evaluation set:
- Loss: 2.3793

## Model description

This model is a casual language modeling GPT2 fine-tuned on scientific articles about algorithmic bias, in an attempt to showcase an example about correcting for algorithmic bias.

## Intended uses & limitations

This model is intended for prompts about algorithms and bias. Other prompts will yield results, but they are less likely to be influenced by the fine-tuning.

## Training and evaluation data

This model is trained on fully freely accessible articles obtained from a PubMed Central search on algorithmic bias. The pmc_result_algorithmicbias.txt file contains the list of PMC's used. Due to technical and time limitations, only fine-tuned on the introduction sections, but training on other sections is planned.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5293        | 1.0   | 1071 | 2.3892          |
| 2.4821        | 2.0   | 2142 | 2.3793          |

### Framework versions

- Transformers 4.14.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3


---
language: ar
widget:
 - text: "لكي نتجنب فيروس [MASK]"
---
# arabert_c19: An Arabert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets
**ARABERT COVID-19**  [Arxiv URL](https://arxiv.org/pdf/2105.03143.pdf) is a pretrained (fine-tuned) version of the AraBERT v2 model (https://huggingface.co/aubmindlab/bert-base-arabertv02). The pretraining was done using 1.5 million multi-dialect Arabic tweets regarding the COVID-19 pandemic from the “Large Arabic Twitter Dataset on COVID-19” (https://arxiv.org/abs/2004.04315).
The model can achieve better results for the tasks that deal with multi-dialect Arabic tweets in relation to the COVID-19 pandemic.

# Classification results for multiple tasks including fake-news and hate speech detection when using arabert_c19 and mbert_ar_c19:
For more details refer to the paper (link)

|                                    | arabert  | mbert    | distilbert multi | arabert Covid-19 | mbert Covid-19 |
|------------------------------------|----------|----------|------------------|------------------|----------------|
| Contains hate (Binary)             |   0.8346 |   0.6675 |   0.7145         |   `0.8649`         |   0.8492       |
| Talk about a cure (Binary)         |   0.8193 |   0.7406 |   0.7127         |   0.9055         |   `0.9176`       |
| News or opinion (Binary)           |   0.8987 |   0.8332 |   0.8099         |   `0.9163`         |   0.9116       |
| Contains fake information (Binary) |   0.6415 |   0.5428 |   0.4743         |   `0.7739`         |   0.7228       |

# Preprocessing

```python
from arabert.preprocess import ArabertPreprocessor
model_name="moha/arabert_c19"
arabert_prep = ArabertPreprocessor(model_name=model_name)
text = "للوقايه من عدم انتشار كورونا عليك اولا غسل اليدين بالماء والصابون وتكون عملية الغسل دقيقه تشمل راحة اليد الأصابع التركيز على الإبهام"
arabert_prep.preprocess(text)
```

# Citation

Please cite as:

``` bibtex
@misc{ameur2021aracovid19mfh,
      title={AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset},
      author={Mohamed Seghir Hadj Ameur and Hassina Aliane},
      year={2021},
      eprint={2105.03143},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

# Contacts
**Hadj Ameur**: [Github](https://github.com/MohamedHadjAmeur) | <mohamedhadjameur@gmail.com> | <mhadjameur@cerist.dz>


---
language: ar
widget:
 - text: "للوقايه من عدم انتشار [MASK]"
---
# arabert_c19: An Arabert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets
**ARABERT COVID-19** is a pretrained (fine-tuned) version of the AraBERT v2 model (https://huggingface.co/aubmindlab/bert-base-arabertv02). The pretraining was done using 1.5 million multi-dialect Arabic tweets regarding the COVID-19 pandemic from the “Large Arabic Twitter Dataset on COVID-19” (https://arxiv.org/abs/2004.04315).
The model can achieve better results for the tasks that deal with multi-dialect Arabic tweets in relation to the COVID-19 pandemic.

# Classification results for multiple tasks including fake-news and hate speech detection when using arabert_c19 and mbert_ar_c19:
For more details refer to the paper (link)

|                                    | arabert  | mbert    | distilbert multi | arabert Covid-19 | mbert Covid-19 |
|------------------------------------|----------|----------|------------------|------------------|----------------|
| Contains hate (Binary)             |   0.8346 |   0.6675 |   0.7145         |   `0.8649`         |   0.8492       |
| Talk about a cure (Binary)         |   0.8193 |   0.7406 |   0.7127         |   0.9055         |   `0.9176`       |
| News or opinion (Binary)           |   0.8987 |   0.8332 |   0.8099         |   `0.9163`         |   0.9116       |
| Contains fake information (Binary) |   0.6415 |   0.5428 |   0.4743         |   `0.7739`         |   0.7228       |

# Preprocessing

```python
from arabert.preprocess import ArabertPreprocessor
model_name="moha/arabert_c19"
arabert_prep = ArabertPreprocessor(model_name=model_name)
text = "للوقايه من عدم انتشار كورونا عليك اولا غسل اليدين بالماء والصابون وتكون عملية الغسل دقيقه تشمل راحة اليد الأصابع التركيز على الإبهام"
arabert_prep.preprocess(text)
```

# Contacts
**Hadj Ameur**: [Github](https://github.com/MohamedHadjAmeur) | <mohamedhadjameur@gmail.com> | <mhadjameur@cerist.dz>


---
language: ar
widget:
 - text: "للوقايه من انتشار [MASK]"
---
# mbert_c19: An mbert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets
**mBERT COVID-19** [Arxiv URL](https://arxiv.org/pdf/2105.03143.pdf) is a pretrained (fine-tuned) version of the mBERT model (https://huggingface.co/bert-base-multilingual-cased). The pretraining was done using 1.5 million multi-dialect Arabic tweets regarding the COVID-19 pandemic from the “Large Arabic Twitter Dataset on COVID-19” (https://arxiv.org/abs/2004.04315).
The model can achieve better results for the tasks that deal with multi-dialect Arabic tweets in relation to the COVID-19 pandemic.

# Classification results for multiple tasks including fake-news and hate speech detection when using arabert_c19 and mbert_ar_c19:
For more details refer to the paper (link)

|                                    | arabert  | mbert    | distilbert multi | arabert Covid-19 | mbert Covid-19 |
|------------------------------------|----------|----------|------------------|------------------|----------------|
| Contains hate (Binary)             |   0.8346 |   0.6675 |   0.7145         |   `0.8649`         |   0.8492       |
| Talk about a cure (Binary)         |   0.8193 |   0.7406 |   0.7127         |   0.9055         |   `0.9176`       |
| News or opinion (Binary)           |   0.8987 |   0.8332 |   0.8099         |   `0.9163`         |   0.9116       |
| Contains fake information (Binary) |   0.6415 |   0.5428 |   0.4743         |   `0.7739`         |   0.7228       |

# Preprocessing
```python
from arabert.preprocess import ArabertPreprocessor
model_name="moha/mbert_ar_c19"
arabert_prep = ArabertPreprocessor(model_name=model_name)
text = "للوقايه من عدم انتشار كورونا عليك اولا غسل اليدين بالماء والصابون وتكون عملية الغسل دقيقه تشمل راحة اليد الأصابع التركيز على الإبهام"
arabert_prep.preprocess(text)
```

# Citation

Please cite as:

``` bibtex
@misc{ameur2021aracovid19mfh,
      title={AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset},
      author={Mohamed Seghir Hadj Ameur and Hassina Aliane},
      year={2021},
      eprint={2105.03143},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

# Contacts
**Hadj Ameur**: [Github](https://github.com/MohamedHadjAmeur) | <mohamedhadjameur@gmail.com> | <mhadjameur@cerist.dz>


# XPDNet-brain-af4
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---

This model was used to achieve the 3rd highest submission in terms of PSNR on the fastMRI dataset (see https://fastmri.org/leaderboards/).
It is a base model for acceleration factor 4.
The model uses 25 iterations and a medium MWCNN, and a big sensitivity maps refiner.

## Model description
For more details, see https://arxiv.org/abs/2010.07290.
This section is WIP.

## Intended uses and limitations
This model can be used to reconstruct brain data from Siemens scanner at acceleration factor 4.
It was shown [here](https://arxiv.org/abs/2106.00753), that it can generalize well, although further tests are required.

## How to use
This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark.
After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`.
The framework is TensorFlow.

You can initialize and load the model weights as follows:
```python
import tensorflow as tf

from fastmri_recon.models.subclassed_models.denoisers.proposed_params import get_model_specs
from fastmri_recon.models.subclassed_models.xpdnet import XPDNet

n_primal = 5
model_fun, model_kwargs, n_scales, res = [
        (model_fun, kwargs, n_scales, res)
        for m_name, m_size, model_fun, kwargs, _, n_scales, res in get_model_specs(n_primal=n_primal, force_res=False)
        if m_name == 'MWCNN' and m_size == 'medium'
][0]
model_kwargs['use_bias'] = False
run_params = dict(
    n_primal=n_primal,
    multicoil=True,
    n_scales=n_scales,
    refine_smaps=True,
    refine_big=True,
    res=res,
    output_shape_spec=True,
    n_iter=25,
)
model = XPDNet(model_fun, model_kwargs, **run_params)
kspace_size = [1, 1, 320, 320]
inputs = [
    tf.zeros(kspace_size + [1], dtype=tf.complex64),  # kspace
    tf.zeros(kspace_size, dtype=tf.complex64),  # mask
    tf.zeros(kspace_size, dtype=tf.complex64),  # smaps
    tf.constant([[320, 320]]),  # shape
]
model(inputs)
model.load_weights('model_weights.h5')
```

Using the model is then as simple as:
```python
model([
    kspace,  # shape: [n_slices, n_coils, n_rows, n_cols, 1]
    mask,  # shape: [n_slices, n_coils, n_rows, n_cols]
    smaps,  # shape: [n_slices, n_coils, n_rows, n_cols]
    shape,  # shape: [n_slices, 2]
])
```

## Limitations and bias
The limitations and bias of this model have not been properly investigated.

## Training data
This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/).

## Training procedure
The training procedure is described in https://arxiv.org/abs/2010.07290.
This section is WIP.

## Evaluation results
On the fastMRI validation dataset, the same model with a smaller sensitivity maps refiner gives the following results for 30 validation volumes per contrast:

| Contrast | T1     | T2     | FLAIR  | T1-POST |
|----------|--------|--------|--------|---------|
| PSNR     | 41.56  | 40.68  | 39.60  | 42.53   |
| SSIM     | 0.9506 | 0.9554 | 0.9321 | 0.9683  |

Further results can be seen on the fastMRI leaderboards for the test and challenge dataset: https://fastmri.org/leaderboards/

## Bibtex entry
```
@inproceedings{Ramzi2020d,
archivePrefix = {arXiv},
arxivId = {2010.07290},
author = {Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc},
booktitle = {ISMRM},
eprint = {2010.07290},
pages = {1--4},
title = {{XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge}},
url = {http://arxiv.org/abs/2010.07290},
year = {2021}
}
```


# CascadeNet-OASIS
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- OASIS
---

This model can be used to reconstruct single coil OASIS data with an acceleration factor of 4.

## Model description
For more details, see https://www.mdpi.com/2076-3417/10/5/1816.
This section is WIP.

## Intended uses and limitations
This model can be used to reconstruct single coil brain retrospective data from the OASIS database at acceleration factor 4.
It cannot be used on multi-coil data.

## How to use
This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark.
After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`.
The framework is TensorFlow.

You can initialize and load the model weights as follows:
```python
from fastmri_recon.models.functional_models.cascading import cascade_net

model = cascade_net()
model.load_weights('model_weights.h5')
```

Using the model is then as simple as:
```python
model([
    kspace,  # shape: [n_slices, n_rows, n_cols, 1]
    mask,  # shape: [n_slices, n_rows, n_cols]
])
```

## Limitations and bias
The limitations and bias of this model have not been properly investigated.

## Training data
This model was trained using the [OASIS dataset](https://www.oasis-brains.org/).

## Training procedure
The training procedure is described in https://www.mdpi.com/2076-3417/10/5/1816 for brain data.
This section is WIP.

## Evaluation results
This model was evaluated using the [OASIS dataset](https://www.oasis-brains.org/).

- PSNR: 32.0
- SSIM: 0.887

## Bibtex entry
```
@article{ramzi2020benchmarking,
  title={Benchmarking MRI reconstruction neural networks on large public datasets},
  author={Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc},
  journal={Applied Sciences},
  volume={10},
  number={5},
  pages={1816},
  year={2020},
  publisher={Multidisciplinary Digital Publishing Institute}
}
```


# UPDNet-knee-af4
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---

This model was used to achieve the 9th highest submission in terms of PSNR on the fastMRI dataset (see https://fastmri.org/leaderboards/) (0.2dB behind the 2nd submission).
It is a base model for acceleration factor 4.
The model uses 25 iterations and a medium-ca-prelu U-net, and a medium sensitivity maps refiner.

## Model description
For more details, see https://arxiv.org/abs/2010.07290.
This section is WIP.

## Intended uses and limitations
This model can be used to reconstruct knee data from Siemens scanner at acceleration factor 4.

## How to use
This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark.
After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`.
The framework is TensorFlow.

You can initialize and load the model weights as follows:
```python
import tensorflow as tf

from fastmri_recon.models.subclassed_models.updnet import UPDNet

model = UPDNet(
    multicoil=True,
    n_dual=1,
    primal_only=True,
    n_layers=4,
    n_iter=25,
    channel_attention_kwargs={'dense': True},
    refine_smaps=True,
    non_linearity='prelu',
    layers_n_channels=[16 * 2**i for i in range(4)],
)
kspace_size = [1, 1, 320, 320]
inputs = [
    tf.zeros(kspace_size + [1], dtype=tf.complex64),  # kspace
    tf.zeros(kspace_size, dtype=tf.complex64),  # mask
    tf.zeros(kspace_size, dtype=tf.complex64),  # smaps
]
model(inputs)
model.load_weights('model_weights.h5')
```

Using the model is then as simple as:
```python
model([
    kspace,  # shape: [n_slices, n_coils, n_rows, n_cols, 1]
    mask,  # shape: [n_slices, n_coils, n_rows, n_cols]
    smaps,  # shape: [n_slices, n_coils, n_rows, n_cols]
])
```

## Limitations and bias
The limitations and bias of this model have not been properly investigated.

## Training data
This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/).

## Training procedure
The training procedure is described in https://arxiv.org/abs/2010.07290.
This section is WIP.

## Evaluation results
No evaluation available outside the one from the fastMRI leaderboard (id: `updnet_v3`).

## Bibtex entry
```
@inproceedings{Ramzi2020d,
archivePrefix = {arXiv},
arxivId = {2010.07290},
author = {Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc},
booktitle = {ISMRM},
eprint = {2010.07290},
pages = {1--4},
title = {{XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge}},
url = {http://arxiv.org/abs/2010.07290},
year = {2021}
}
```


# NCPDNet-multicoil-radial
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---

This is a non-Cartesian multicoil MRI reconstruction model for radial trajectories at acceleration factor 4.
The model uses 10 iterations and a small vanilla CNN.

## Model description
For more details, see https://hal.inria.fr/hal-03188997.
This section is WIP.

## Intended uses and limitations
This model can be used to reconstruct multicoil knee data from Siemens scanner at acceleration factor 4 in a radial acquisition setting.

## How to use
This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark.
After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`.
The framework is TensorFlow.

You can initialize and load the model weights as follows:
```python
import tensorflow as tf

from fastmri_recon.models.subclassed_models.ncpdnet import NCPDNet

model = NCPDNet(
    multicoil=True,
    im_size=(640, 400),
    dcomp=True,
    refine_smaps=True,
)
kspace_shape = 1
inputs = [
    tf.zeros([1, 1, kspace_shape, 1], dtype=tf.complex64),
    tf.zeros([1, 2, kspace_shape], dtype=tf.float32),
    tf.zeros([1, 1, 640, 320], dtype=tf.complex64),
    (tf.constant([320]), tf.ones([1, kspace_shape], dtype=tf.float32)),
]
model(inputs)
model.load_weights('model_weights.h5')
```

Using the model is then as simple as:
```python
model([
    kspace,  # shape: [n_slices, n_coils, n_kspace_samples, 1]
    traj,  # shape: [n_slices, n_coils, 2, n_kspace_samples]
    smaps,  # shape: [n_slices, n_coils, n_kspace_samples, n_coils]
    (
        output_shape,  # shape: [n_slices, 1]
        dcomp,  # shape: [n_slices, n_kspace_samples]
    )
])
```

## Limitations and bias
The limitations and bias of this model have not been properly investigated.

## Training data
This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/).

## Training procedure
The training procedure is described in https://hal.inria.fr/hal-03188997.
This section is WIP.

## Evaluation results
On the fastMRI validation dataset:
- PSNR: 40.00
- SSIM: 0.9191

## Bibtex entry
```
@unpublished{ramzi:hal-03188997,
  TITLE = {{NC-PDNet: a Density-Compensated Unrolled Network for 2D and 3D non-Cartesian MRI Reconstruction}},
  AUTHOR = {Ramzi, Zaccharie and G R, Chaithya and Starck, Jean-Luc and Ciuciu, Philippe},
  YEAR = {2021},
  MONTH = Sep,
}
```