license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'mr', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100.0 - mixed_precision_training: Native AMP | 2396ef1526c5678ccf7f9c25e48f08f9 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'mr', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.671 | 22.73 | 500 | 1.3618 | 0.9499 | | 1.1599 | 45.45 | 1000 | 0.6330 | 0.6627 | | 0.8252 | 68.18 | 1500 | 0.6226 | 0.6426 | | 0.6424 | 90.91 | 2000 | 0.6359 | 0.6041 | | cd7cc7843de9dbabb6c4ea08d947ba76 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet pip install -e . cd egs2/dsing/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/ftshijt_espnet2_asr_dsing_transformer ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | ff8064c0a078c2be5562abb3b17a9ffd |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Sun Mar 20 00:28:37 EDT 2022` - python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.10.1` - Git hash: `c1ed71c6899e54c0b3dad82687886b1183cd0885` - Commit date: `Wed Mar 16 23:34:49 2022 -0400` | b7432f949c9e903719a336817111700d |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_bpe500_valid.loss.ave_asr_model_valid.acc.ave/dev|482|4018|77.0|16.2|6.8|4.0|27.0|65.1| |decode_asr_lm_lm_train_lm_bpe500_valid.loss.ave_asr_model_valid.acc.ave/test|480|4632|76.1|17.3|6.6|3.7|27.6|57.7| | b333ae41bf7027eb9e7b7fbc90be5b80 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_bpe500_valid.loss.ave_asr_model_valid.acc.ave/dev|482|18692|85.0|5.8|9.2|4.2|19.2|65.1| |decode_asr_lm_lm_train_lm_bpe500_valid.loss.ave_asr_model_valid.acc.ave/test|480|21787|84.9|6.3|8.8|4.2|19.3|57.7| | b31f2ba1341c8005919f1bdda3f7e7ef |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_bpe500_valid.loss.ave_asr_model_valid.acc.ave/dev|482|6097|75.2|12.8|12.0|4.1|28.9|65.1| |decode_asr_lm_lm_train_lm_bpe500_valid.loss.ave_asr_model_valid.acc.ave/test|480|7736|75.3|14.3|10.4|4.1|28.8|57.7| | e05e3ba4c180b55e1644a7521a71a9b2 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_asr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_raw_bpe500_sp 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: 100 patience: 15 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 grad_clip_type: 2.0 grad_noise: false accum_grad: 2 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: 32 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_bpe500_sp/train/speech_shape - exp/asr_stats_raw_bpe500_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_bpe500_sp/valid/speech_shape - exp/asr_stats_raw_bpe500_sp/valid/text_shape.bpe 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/train30_sp/wav.scp - speech - kaldi_ark - - dump/raw/train30_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - kaldi_ark - - 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: 1.0 scheduler: noamlr scheduler_conf: warmup_steps: 25000 token_list: - <blank> - <unk> - ▁I - '''' - ▁YOU - S - T - ▁THE - M - ▁ME - ▁A - ▁AND - ▁TO - E - A - ING - D - ▁MY - ▁ - O - ▁IT - I - N - RE - Y - ▁BE - ▁IN - ▁ON - ▁LOVE - U - ▁WE - LL - H - ▁YOUR - ▁S - IN - ▁OF - ▁DO - ▁THAT - ▁ALL - L - ▁DON - ▁OH - ▁LIKE - ▁KNOW - ▁FOR - ▁CAN - ▁JUST - P - ▁BUT - ED - K - ▁WHEN - ▁SO - R - ▁GO - ▁WHAT - ▁C - ▁WITH - W - ▁F - C - ▁NO - ER - ▁ONE - ▁LET - VE - ES - ▁NOW - ▁BABY - G - ▁GOT - ▁COME - CAUSE - LE - B - ▁B - AR - ▁UP - ▁' - ▁W - ▁SEE - ▁TIME - ▁ARE - ▁G - ▁LOOK - ▁THIS - F - ▁IS - ▁NEVER - ▁M - ▁P - AN - ▁WAS - ▁WAY - ▁IF - OR - ▁SAY - V - ▁R - ▁T - ▁DOWN - RA - ▁THERE - ▁HEART - ▁NOT - RO - ▁WILL - ▁OUT - CE - ▁WANT - ▁YEAH - ▁HAVE - ▁GIVE - ▁TOO - ▁GONNA - ▁HOW - ▁NEED - ▁GET - ▁TAKE - ▁EVERY - ▁FEEL - ▁HE - EN - ▁FROM - ▁HA - ▁K - ▁SHE - 'ON' - ▁DI - RI - ▁ONLY - NE - ▁WHO - ▁AWAY - ▁E - ▁D - ▁LIFE - ▁MAKE - IC - ▁BACK - ▁WHERE - ▁MADE - ▁DAY - ▁HERE - ▁LO - ▁HER - ▁AS - ▁GOOD - ▁WANNA - ▁OOH - ▁TELL - LY - TH - ▁WON - ▁LIGHT - ▁KEEP - ▁MA - ▁LA - ▁SH - ▁WORLD - ▁MORE - ▁LI - AL - ▁COULD - ▁GIRL - ▁NOTHING - ▁EVER - ▁THINK - IE - ▁BY - ▁AT - ▁TONIGHT - ▁THEY - ▁CALL - ▁HO - ▁WOULD - IL - ▁OUR - ▁FALL - ▁NIGHT - ▁THAN - ▁DE - ▁SOME - ▁WAIT - ▁RIGHT - ▁RE - ▁HALLELUJAH - ▁TH - NG - ▁CO - ▁WERE - ▁TALK - ET - ▁BO - ▁HOLD - UR - ▁BEEN - ▁US - ▁PA - VER - ▁EYES - ▁DREAM - ▁SONG - ▁SHOULD - ▁STILL - ▁OVER - TA - ▁ANYMORE - IGHT - ▁STAY - ▁BETTER - LESS - ▁THROUGH - ▁LITTLE - X - ▁GONE - ▁AIN - ▁DA - ▁HOLDING - ▁HURT - ▁TRY - ▁FIND - Z - DE - ▁LAST - ▁SAID - ▁ALWAYS - ▁BODY - ▁MIND - ▁CRY - ▁EVEN - ▁RUN - ▁HOPE - ▁WITHOUT - ▁MISS - ▁ABOUT - ▁HAND - ▁J - ▁AGAIN - ▁THOUGH - ▁NAH - ▁LIVE - ▁BA - ▁OLD - ▁HEAD - ▁FIRE - ▁MAN - ▁SOMETHING - ▁WHY - THER - ▁HOME - ▁OR - ▁INSIDE - ▁NEW - ▁HEY - TION - ▁EVERYTHING - ▁HAD - ▁SOMETIMES - ▁HARD - ▁TOUCH - ▁HEAR - ▁AM - ▁MUCH - ▁LONG - ▁STAR - GETTING - ▁WALK - ▁PEOPLE - ▁BEFORE - ▁CLOSE - ▁TWO - ▁FAR - ▁SHOW - ▁STAND - ▁LOSE - ▁HELP - ▁NAME - ▁BOY - ▁TRUE - ▁PLAY - ▁DARK - ▁THINGS - ▁NA - ▁TEAR - ▁END - ▁NOBODY - ▁SEA - ▁ROCKABYE - ▁BELIEVE - ▁BROKE - ▁AROUND - ▁START - ▁KISS - ▁FEELING - ▁BREAK - ▁SOMEONE - ▁FRIEND - ▁ALONE - ▁BEAUTIFUL - ▁CRAZY - ▁OWN - OSE - ▁STOP - ▁LOST - ▁HIM - ▁BAD - ▁CHANCE - ▁REALLY - ▁WISH - ▁MOVE - ▁SKY - ▁PLACE - AKE - ▁LEAVE - ▁YA - ▁STRONG - ▁PUT - ▁OPEN - ▁WRONG - ▁COLD - OCK - ▁USED - ▁FOUND - ▁LONELY - ▁DANCE - EACH - ▁ANOTHER - ▁SIDE - ▁UNDER - ▁MATTER - ▁THESE - ▁CARE - ▁MINE - ▁SHINE - ▁AFRAID - ▁TURN - ▁PLEASE - ▁SUN - ▁DIAMOND - ▁UNTIL - ▁FACE - ▁LEARN - ▁TRUST - ▁WONDER - ▁BREATH - ATE - ▁SORRY - ▁HU - ▁WATCH - ▁LATE - ROUND - ▁ARMS - ▁PERFECT - ▁MAYBE - ▁PULL - ▁REMEMBER - ▁FIGHT - ▁MYSELF - ▁INTO - ▁DARLING - ▁THUNDER - ▁FOLLOW - ▁REASON - ▁BURN - ▁HIS - ▁MUST - ▁FREE - ▁FLASHLIGHT - ▁1 - ▁ENOUGH - ▁DRINK - ▁WORDS - ▁HIDE - ▁UN - ▁FORGET - ▁SURE - ▁CHANGE - ▁SMILE - ▁PROMISE - ▁FOREVER - '2' - ▁SWEET - ▁SAME - ▁OOOH - ▁PART - ▁SOMEBODY - NESS - ▁BRIGHT - ▁HEAVEN - ▁DEEP - ▁HIGH - ▁INSTEAD - ▁MOMENT - ▁ALONG - ▁ALRIGHT - ▁SLOW - ▁TOMORROW - ▁SOUL - ▁QU - ▁PUSH - ▁CHANDELIER - ▁LEFT - SIDE - ▁TOLD - ▁KNEW - READY - ▁LOVING - ▁SAW - '3' - ▁WORK - ▁DANCING - ▁THREE - ▁SAVE - ▁SHOOT - ▁LEAD - ▁SKI - ▁WILD - ▁WIND - ▁WHILE - ▁EDGE - ▁HAPPY - ▁FEAR - STUCK - ▁MOST - ▁LISTEN - ▁WOAH - ▁FIRST - ▁JOLENE - ▁VOICE - ▁COMP - ▁MILLION - FUL - ▁OOOOOH - ▁CAME - ▁RISE - ▁NEXT - ▁COUNT - ▁MOUNTAIN - ▁ROOM - ▁BLUE - ▁HIT - ▁RAISE - J - ▁THOUSAND - ▁SHAP - ▁TREAT - ▁DRY - ▁FINALLY - ▁TITANIUM - ▁CARRY - ▁TRUTH - ▁WATER - ▁MORNING - TIME - ▁BELONG - ▁UMA - ▁ALIVE - ▁ELSE - ▁ANGEL - ▁BRAND - ▁APART - ▁EVERYBODY - ▁SOUND - ▁GUESS - ▁PRAY - ▁FAITH - ▁AFTER - ▁THROW - ▁TRIED - ▁SLEEP - ▁FOOL - ▁DISCOVERING - ▁FUCK - ▁TASTE - ▁UNDERSTAND - ▁SHAME - ▁POWER - ▁WELCOME - ▁FELT - ▁SAFE - ▁DESERVE - ▁GAME - ▁SUPERMA - ▁SWEAR - ▁BETWEEN - ▁GLASS - ▁CATCH - ▁TOGETHER - '0' - '4' - '6' - '5' - '1' - '8' - '7' - '9' - Q - <sos/eos> init: xavier_uniform 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 use_preprocessor: true token_type: bpe bpemodel: data/token_list/bpe_unigram500/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: default frontend_conf: fs: 16k specaug: null specaug_conf: {} normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_bpe500_sp/train/feats_stats.npz preencoder: null preencoder_conf: {} encoder: transformer encoder_conf: input_layer: conv2d num_blocks: 12 linear_units: 2048 dropout_rate: 0.1 output_size: 256 attention_heads: 4 attention_dropout_rate: 0.0 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: input_layer: embed num_blocks: 6 linear_units: 2048 dropout_rate: 0.1 required: - output_dir - token_list version: 0.10.7a1 distributed: false ``` </details> | c4dd0450ce5cea3726ec629f4b43ddee |
apache-2.0 | ['generated_from_trainer'] | false | 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: 0.2292 - Accuracy: 0.926 - F1: 0.9259 | 9cca8f79d0a404810331173fcaca2e9a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8732 | 1.0 | 250 | 0.3363 | 0.903 | 0.9002 | | 0.2645 | 2.0 | 500 | 0.2292 | 0.926 | 0.9259 | | f0cd6854a4d2a88c81b9890231bccf2d |
apache-2.0 | ['thai', 'question-answering', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pretrained on Thai Wikipedia texts for dependency-parsing (head-detection on Universal Dependencies) as question-answering, derived from [roberta-base-thai-spm](https://huggingface.co/KoichiYasuoka/roberta-base-thai-spm). Use [MASK] inside `context` to avoid ambiguity when specifying a multiple-used word as `question`. | fac79dd41a333d9d49526c128ac76bf1 |
apache-2.0 | ['thai', 'question-answering', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForQuestionAnswering,QuestionAnsweringPipeline tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-thai-spm-ud-head") model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/roberta-base-thai-spm-ud-head") qap=QuestionAnsweringPipeline(tokenizer=tokenizer,model=model,align_to_words=False) print(qap(question="กว่า",context="หลายหัวดีกว่าหัวเดียว")) ``` or (with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/)) ```py class TransformersUD(object): def __init__(self,bert): import os from transformers import (AutoTokenizer,AutoModelForQuestionAnswering, AutoModelForTokenClassification,AutoConfig,TokenClassificationPipeline) self.tokenizer=AutoTokenizer.from_pretrained(bert) self.model=AutoModelForQuestionAnswering.from_pretrained(bert) x=AutoModelForTokenClassification.from_pretrained if os.path.isdir(bert): d,t=x(os.path.join(bert,"deprel")),x(os.path.join(bert,"tagger")) else: from transformers.utils import cached_file c=AutoConfig.from_pretrained(cached_file(bert,"deprel/config.json")) d=x(cached_file(bert,"deprel/pytorch_model.bin"),config=c) s=AutoConfig.from_pretrained(cached_file(bert,"tagger/config.json")) t=x(cached_file(bert,"tagger/pytorch_model.bin"),config=s) self.deprel=TokenClassificationPipeline(model=d,tokenizer=self.tokenizer, aggregation_strategy="simple") self.tagger=TokenClassificationPipeline(model=t,tokenizer=self.tokenizer) def __call__(self,text): import numpy,torch,ufal.chu_liu_edmonds w=[(t["start"],t["end"],t["entity_group"]) for t in self.deprel(text)] z,n={t["start"]:t["entity"].split("|") for t in self.tagger(text)},len(w) r,m=[text[s:e] for s,e,p in w],numpy.full((n+1,n+1),numpy.nan) v,c=self.tokenizer(r,add_special_tokens=False)["input_ids"],[] for i,t in enumerate(v): q=[self.tokenizer.cls_token_id]+t+[self.tokenizer.sep_token_id] c.append([q]+v[0:i]+[[self.tokenizer.mask_token_id]]+v[i+1:]+[[q[-1]]]) b=[[len(sum(x[0:j+1],[])) for j in range(len(x))] for x in c] with torch.no_grad(): d=self.model(input_ids=torch.tensor([sum(x,[]) for x in c]), token_type_ids=torch.tensor([[0]*x[0]+[1]*(x[-1]-x[0]) for x in b])) s,e=d.start_logits.tolist(),d.end_logits.tolist() for i in range(n): for j in range(n): m[i+1,0 if i==j else j+1]=s[i][b[i][j]]+e[i][b[i][j+1]-1] h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] if [0 for i in h if i==0]!=[0]: i=([p for s,e,p in w]+["root"]).index("root") j=i+1 if i<n else numpy.nanargmax(m[:,0]) m[0:j,0]=m[j+1:,0]=numpy.nan h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] u=" | 52e6ee4750de827eb292265e8de371e1 |
apache-2.0 | ['thai', 'question-answering', 'dependency-parsing'] | false | text = "+text.replace("\n"," ")+"\n" for i,(s,e,p) in enumerate(w,1): p="root" if h[i]==0 else "dep" if p=="root" else p u+="\t".join([str(i),r[i-1],"_",z[s][0][2:],"_","|".join(z[s][1:]), str(h[i]),p,"_","_" if i<n and e<w[i][0] else "SpaceAfter=No"])+"\n" return u+"\n" nlp=TransformersUD("KoichiYasuoka/roberta-base-thai-spm-ud-head") print(nlp("หลายหัวดีกว่าหัวเดียว")) ``` | 0b4c9d3599586bf57fcf43a066701a2d |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | marian-finetuned-kde4-en-to-zh This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.9338 - Bleu: 40.6780 | b961a1d9a963ad0f109e6d74dea3f777 |
creativeml-openrail-m | ['text-to-image'] | false | noggles9000 on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook | 225bbe18a4d789a132075bc054ed0764 |
creativeml-openrail-m | ['text-to-image'] | false | Model by alxdfy This your the Stable Diffusion model fine-tuned the noggles9000 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt(s)`: **nounfootball.jpg** You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb). You can run your new concept via A1111 Colab :[Fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Sample pictures of this concept: nounfootball.jpg  | f444f267e5925df8ee214d769e209dbe |
mit | [] | false | Base model: [gpt2-large](https://huggingface.co/gpt2-large) Fine-tuned to generate responses on a dataset of [COVID-19 public health tweets](https://github.com/TheRensselaerIDEA/generative-response-modeling). For more information about the dataset, task and training, see [our paper](https://arxiv.org/abs/2204.04353). This checkpoint corresponds to the lowest validation perplexity (3.36 at 2 epochs) seen during training. See Training metrics for Tensorboard logs. Also see: our [Vaccine public health tweet response model](https://huggingface.co/TheRensselaerIDEA/gpt2-large-vaccine-tweet-response). **Data input format:** <span style="color:red"><|message|></span>public health message<span style="color:red"><|author|></span>public health Twitter handle<span style="color:red"><|response|></span> Example: ```python from transformers import AutoTokenizer, AutoModelForCausalLM from transformers.trainer_utils import set_seed import torch tokenizer = AutoTokenizer.from_pretrained("TheRensselaerIDEA/gpt2-large-covid-tweet-response") model = AutoModelForCausalLM.from_pretrained("TheRensselaerIDEA/gpt2-large-covid-tweet-response") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) set_seed(33) message = "Is your child worried about | d959c95b2e01f3a5d2270c392ea91510 |
mit | [] | false | COVID19? Learn the facts so you can answer your children’s questions." author = "CDCgov" num_responses = 2 author_token, message_token, response_token = tokenizer.additional_special_tokens input_str = f"{message_token}{message}{author_token}{author}{response_token}" inputs = tokenizer(input_str, return_tensors="pt").to(device) responses_ids = model.generate(**inputs, max_new_tokens=100, pad_token_id=tokenizer.pad_token_id, do_sample=True, top_p=0.95, temperature=1.5, num_beams=3, early_stopping=True, num_return_sequences=num_responses) responses = [tokenizer.decode(r[inputs.input_ids.shape[-1]:], skip_special_tokens=True) for r in responses_ids] for i, resp in enumerate(responses): print(f"Response {i}: {resp}\n") ``` Output: ``` Response 0: @CDCgov I'm not worried. I don't know who needs to hear this, but I have a feeling I know who will be listening. It is not the virus. It is the media. I know you and CDC have been lying for months now, but the media will keep pushing this lie. Response 1: | be996e59edd7c52704697dde2ffa14b1 |
cc-by-4.0 | [] | false | MahaBERT MahaBERT is a Marathi BERT model. It is a multilingual BERT (bert-base-multilingual-cased) model fine-tuned on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets. [dataset link] (https://github.com/l3cube-pune/MarathiNLP) More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2202.01159) New version of this model is available here: https://huggingface.co/l3cube-pune/marathi-bert-v2 ``` @InProceedings{joshi:2022:WILDRE6, author = {Joshi, Raviraj}, title = {L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources}, booktitle = {Proceedings of The WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference}, month = {June}, year = {2022}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {97--101} } ``` | 91bdc6dab907891ec5b87fc99be27745 |
apache-2.0 | ['Token Classification'] | false | Model description
This model is a fine-tuned version of macbert for the purpose of spell checking in medical application scenarios. We fine-tuned macbert Chinese base version on a 300M dataset including 60K+ authorized medical articles. We proposed to randomly confuse 30% sentences of these articles by adding noise with a either visually or phonologically resembled characters. Consequently, the fine-tuned model can achieve 96% accuracy on our test dataset.
| 319ef8f16b5e80c7fe19d8f9ed922fd3 |
apache-2.0 | ['Token Classification'] | false | Intended uses & limitations
You can use this model directly with a pipeline for token classification:
```python
>>> from transformers import (AutoModelForTokenClassification, AutoTokenizer)
>>> from transformers import pipeline
>>> hub_model_id = "9pinus/macbert-base-chinese-medical-collation"
>>> model = AutoModelForTokenClassification.from_pretrained(hub_model_id)
>>> tokenizer = AutoTokenizer.from_pretrained(hub_model_id)
>>> classifier = pipeline('ner', model=model, tokenizer=tokenizer)
>>> result = classifier("如果病情较重，可适当口服甲肖唑片、环酯红霉素片等药物进行抗感染镇痛。")
>>> for item in result:
>>> if item['entity'] == 1:
>>> print(item)
{'entity': 1, 'score': 0.58127016, 'index': 14, 'word': '肖', 'start': 13, 'end': 14}
```
| ca6cb2e99279a44de2d478d81145b692 |
apache-2.0 | ['generated_from_trainer'] | false | 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: 1 | 9321b95e46f7b78beae774191dc7a74e |
apache-2.0 | ['generated_from_trainer'] | false | rule_learning_test This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the enoriega/odinsynth_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.1255 | d6f663b089ee7befb48ed81698fcebaa |
apache-2.0 | ['generated_from_trainer'] | false | 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: 1000 - total_train_batch_size: 8000 - 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 | 2575198f9ea6a932b0e067e2eea2bbef |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1764 | 0.32 | 20 | 0.2303 | | 0.145 | 0.64 | 40 | 0.1470 | | 0.129 | 0.96 | 60 | 0.1321 | | 0.1256 | 1.29 | 80 | 0.1265 | | 0.1304 | 1.61 | 100 | 0.1252 | | 0.1235 | 1.93 | 120 | 0.1260 | | 0.125 | 2.26 | 140 | 0.1261 | | 0.1263 | 2.58 | 160 | 0.1262 | | 0.1244 | 2.9 | 180 | 0.1256 | | aef0af20a781cba8582fc61ed1904491 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-tamil-colab-final This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.7539 - Wer: 0.6135 | f00e6130efe77707b857888b42ae474e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 11.1466 | 1.0 | 118 | 4.3444 | 1.0 | | 3.4188 | 2.0 | 236 | 3.2496 | 1.0 | | 2.8617 | 3.0 | 354 | 1.6165 | 1.0003 | | 0.958 | 4.0 | 472 | 0.7984 | 0.8720 | | 0.5929 | 5.0 | 590 | 0.6733 | 0.7831 | | 0.4628 | 6.0 | 708 | 0.6536 | 0.7621 | | 0.3834 | 7.0 | 826 | 0.6037 | 0.7155 | | 0.3242 | 8.0 | 944 | 0.6376 | 0.7184 | | 0.2736 | 9.0 | 1062 | 0.6214 | 0.7070 | | 0.2433 | 10.0 | 1180 | 0.6158 | 0.6944 | | 0.2217 | 11.0 | 1298 | 0.6548 | 0.6830 | | 0.1992 | 12.0 | 1416 | 0.6331 | 0.6775 | | 0.1804 | 13.0 | 1534 | 0.6644 | 0.6874 | | 0.1639 | 14.0 | 1652 | 0.6629 | 0.6649 | | 0.143 | 15.0 | 1770 | 0.6927 | 0.6836 | | 0.1394 | 16.0 | 1888 | 0.6933 | 0.6888 | | 0.1296 | 17.0 | 2006 | 0.7039 | 0.6860 | | 0.1212 | 18.0 | 2124 | 0.7042 | 0.6628 | | 0.1121 | 19.0 | 2242 | 0.7132 | 0.6475 | | 0.1069 | 20.0 | 2360 | 0.7423 | 0.6438 | | 0.1063 | 21.0 | 2478 | 0.7171 | 0.6484 | | 0.1025 | 22.0 | 2596 | 0.7396 | 0.6451 | | 0.0946 | 23.0 | 2714 | 0.7400 | 0.6432 | | 0.0902 | 24.0 | 2832 | 0.7385 | 0.6286 | | 0.0828 | 25.0 | 2950 | 0.7368 | 0.6286 | | 0.079 | 26.0 | 3068 | 0.7471 | 0.6306 | | 0.0747 | 27.0 | 3186 | 0.7524 | 0.6201 | | 0.0661 | 28.0 | 3304 | 0.7576 | 0.6201 | | 0.0659 | 29.0 | 3422 | 0.7579 | 0.6130 | | 0.0661 | 30.0 | 3540 | 0.7539 | 0.6135 | | b5be87ce17184220cdb653e074c1db4c |
creativeml-openrail-m | ['text-to-image'] | false | training params ```json { "pretrained_model_name_or_path": "runwayml/stable-diffusion-v1-5", "instance_data_dir": "./b3d0ef12-11d6-43df-8a96-ebcb5ca71ea1/instance_data", "class_data_dir": "./class_data/person", "output_dir": "./b3d0ef12-11d6-43df-8a96-ebcb5ca71ea1/", "train_text_encoder": true, "with_prior_preservation": true, "prior_loss_weight": 1.0, "instance_prompt": "me", "class_prompt": "person", "resolution": 512, "train_batch_size": 1, "gradient_accumulation_steps": 1, "gradient_checkpointing": true, "use_8bit_adam": true, "learning_rate": 1e-06, "lr_scheduler": "polynomial", "lr_warmup_steps": 0, "num_class_images": 500, "max_train_steps": 1050, "mixed_precision": "fp16" } ``` | c5f108850baa38ce9929cd4229f196d8 |
mit | ['generated_from_trainer'] | false | BERiT_2000_custom_architecture_40_epochs_ls_.2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.3120 | b7fbeba08a91a9ec76465bf384fe02d1 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - 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: 40 - label_smoothing_factor: 0.2 | 7f3e84fa4608363c4876dabe97e08971 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 15.998 | 0.19 | 500 | 8.5537 | | 7.8818 | 0.39 | 1000 | 7.3646 | | 7.2781 | 0.58 | 1500 | 7.1307 | | 7.1073 | 0.77 | 2000 | 7.0462 | | 7.0749 | 0.97 | 2500 | 7.0667 | | 7.0373 | 1.16 | 3000 | 6.9511 | | 6.9767 | 1.36 | 3500 | 6.8339 | | 6.9483 | 1.55 | 4000 | 6.7795 | | 6.9071 | 1.74 | 4500 | 6.7828 | | 6.8591 | 1.94 | 5000 | 6.7164 | | 6.8595 | 2.13 | 5500 | 6.7705 | | 6.8406 | 2.32 | 6000 | 6.6906 | | 6.7861 | 2.52 | 6500 | 6.6878 | | 6.8103 | 2.71 | 7000 | 6.6486 | | 6.7724 | 2.9 | 7500 | 6.6703 | | 6.7563 | 3.1 | 8000 | 6.6626 | | 6.7567 | 3.29 | 8500 | 6.6603 | | 6.7315 | 3.49 | 9000 | 6.6392 | | 6.7443 | 3.68 | 9500 | 6.6306 | | 6.7244 | 3.87 | 10000 | 6.6456 | | 6.7464 | 4.07 | 10500 | 6.6224 | | 6.7008 | 4.26 | 11000 | 6.6138 | | 6.7076 | 4.45 | 11500 | 6.6783 | | 6.6944 | 4.65 | 12000 | 6.6147 | | 6.6993 | 4.84 | 12500 | 6.6466 | | 6.6893 | 5.03 | 13000 | 6.6369 | | 6.6905 | 5.23 | 13500 | 6.6293 | | 6.6899 | 5.42 | 14000 | 6.6271 | | 6.6835 | 5.62 | 14500 | 6.6566 | | 6.6746 | 5.81 | 15000 | 6.6385 | | 6.68 | 6.0 | 15500 | 6.6309 | | 6.6776 | 6.2 | 16000 | 6.6069 | | 6.6714 | 6.39 | 16500 | 6.5991 | | 6.6766 | 6.58 | 17000 | 6.6180 | | 6.6591 | 6.78 | 17500 | 6.6212 | | 6.6396 | 6.97 | 18000 | 6.5804 | | 6.6575 | 7.16 | 18500 | 6.6096 | | 6.6506 | 7.36 | 19000 | 6.5579 | | 6.6618 | 7.55 | 19500 | 6.5911 | | 6.6581 | 7.75 | 20000 | 6.5870 | | 6.6703 | 7.94 | 20500 | 6.6062 | | 6.6392 | 8.13 | 21000 | 6.5962 | | 6.6343 | 8.33 | 21500 | 6.5903 | | 6.6426 | 8.52 | 22000 | 6.6010 | | 6.6227 | 8.71 | 22500 | 6.6060 | | 6.6392 | 8.91 | 23000 | 6.5935 | | 6.6198 | 9.1 | 23500 | 6.6293 | | 6.6372 | 9.3 | 24000 | 6.5594 | | 6.6146 | 9.49 | 24500 | 6.5917 | | 6.6119 | 9.68 | 25000 | 6.5694 | | 6.6292 | 9.88 | 25500 | 6.6230 | | 6.634 | 10.07 | 26000 | 6.5857 | | 6.5863 | 10.26 | 26500 | 6.5938 | | 6.5957 | 10.46 | 27000 | 6.6256 | | 6.5928 | 10.65 | 27500 | 6.6111 | | 6.5948 | 10.84 | 28000 | 6.6031 | | 6.6131 | 11.04 | 28500 | 6.5582 | | 6.5946 | 11.23 | 29000 | 6.6093 | | 6.6155 | 11.43 | 29500 | 6.5670 | | 6.6051 | 11.62 | 30000 | 6.6016 | | 6.5917 | 11.81 | 30500 | 6.6045 | | 6.5918 | 12.01 | 31000 | 6.5802 | | 6.558 | 12.2 | 31500 | 6.5195 | | 6.5896 | 12.39 | 32000 | 6.6315 | | 6.5662 | 12.59 | 32500 | 6.6112 | | 6.5702 | 12.78 | 33000 | 6.5779 | | 6.5798 | 12.97 | 33500 | 6.5662 | | 6.5963 | 13.17 | 34000 | 6.5776 | | 6.5733 | 13.36 | 34500 | 6.5870 | | 6.5499 | 13.56 | 35000 | 6.5850 | | 6.5492 | 13.75 | 35500 | 6.5957 | | 6.5466 | 13.94 | 36000 | 6.5812 | | 6.5741 | 14.14 | 36500 | 6.5287 | | 6.5612 | 14.33 | 37000 | 6.5611 | | 6.5648 | 14.52 | 37500 | 6.5381 | | 6.5661 | 14.72 | 38000 | 6.5742 | | 6.5564 | 14.91 | 38500 | 6.5424 | | 6.5423 | 15.1 | 39000 | 6.5987 | | 6.5471 | 15.3 | 39500 | 6.5662 | | 6.5559 | 15.49 | 40000 | 6.5290 | | 6.5332 | 15.69 | 40500 | 6.5412 | | 6.5362 | 15.88 | 41000 | 6.5486 | | 6.5351 | 16.07 | 41500 | 6.5959 | | 6.5337 | 16.27 | 42000 | 6.5405 | | 6.5246 | 16.46 | 42500 | 6.5217 | | 6.4999 | 16.65 | 43000 | 6.5443 | | 6.5459 | 16.85 | 43500 | 6.5424 | | 6.5077 | 17.04 | 44000 | 6.5499 | | 6.5069 | 17.23 | 44500 | 6.5509 | | 6.5189 | 17.43 | 45000 | 6.5310 | | 6.5086 | 17.62 | 45500 | 6.5361 | | 6.5182 | 17.82 | 46000 | 6.5320 | | 6.51 | 18.01 | 46500 | 6.4850 | | 6.4868 | 18.2 | 47000 | 6.5155 | | 6.4665 | 18.4 | 47500 | 6.5305 | | 6.5123 | 18.59 | 48000 | 6.5301 | | 6.4981 | 18.78 | 48500 | 6.4617 | | 6.4606 | 18.98 | 49000 | 6.4895 | | 6.4716 | 19.17 | 49500 | 6.4790 | | 6.4733 | 19.36 | 50000 | 6.4818 | | 6.4935 | 19.56 | 50500 | 6.4518 | | 6.4761 | 19.75 | 51000 | 6.4852 | | 6.4651 | 19.95 | 51500 | 6.4836 | | 6.4462 | 20.14 | 52000 | 6.4792 | | 6.4605 | 20.33 | 52500 | 6.4661 | | 6.4718 | 20.53 | 53000 | 6.4639 | | 6.459 | 20.72 | 53500 | 6.4683 | | 6.4407 | 20.91 | 54000 | 6.4663 | | 6.4388 | 21.11 | 54500 | 6.4832 | | 6.4479 | 21.3 | 55000 | 6.4606 | | 6.4583 | 21.49 | 55500 | 6.4723 | | 6.4169 | 21.69 | 56000 | 6.4897 | | 6.4437 | 21.88 | 56500 | 6.4368 | | 6.4566 | 22.08 | 57000 | 6.4491 | | 6.4248 | 22.27 | 57500 | 6.4630 | | 6.431 | 22.46 | 58000 | 6.4246 | | 6.4274 | 22.66 | 58500 | 6.4618 | | 6.4262 | 22.85 | 59000 | 6.4177 | | 6.4328 | 23.04 | 59500 | 6.4243 | | 6.4305 | 23.24 | 60000 | 6.4178 | | 6.4078 | 23.43 | 60500 | 6.4310 | | 6.4431 | 23.63 | 61000 | 6.4338 | | 6.4066 | 23.82 | 61500 | 6.4080 | | 6.417 | 24.01 | 62000 | 6.4236 | | 6.4008 | 24.21 | 62500 | 6.3703 | | 6.4222 | 24.4 | 63000 | 6.4188 | | 6.4304 | 24.59 | 63500 | 6.3924 | | 6.4063 | 24.79 | 64000 | 6.4140 | | 6.4176 | 24.98 | 64500 | 6.4419 | | 6.4203 | 25.17 | 65000 | 6.4250 | | 6.3983 | 25.37 | 65500 | 6.3602 | | 6.3911 | 25.56 | 66000 | 6.4129 | | 6.3821 | 25.76 | 66500 | 6.4225 | | 6.3864 | 25.95 | 67000 | 6.3801 | | 6.4109 | 26.14 | 67500 | 6.4032 | | 6.4136 | 26.34 | 68000 | 6.3870 | | 6.3714 | 26.53 | 68500 | 6.4385 | | 6.3711 | 26.72 | 69000 | 6.4081 | | 6.391 | 26.92 | 69500 | 6.3901 | | 6.3931 | 27.11 | 70000 | 6.4047 | | 6.3842 | 27.3 | 70500 | 6.3830 | | 6.3798 | 27.5 | 71000 | 6.3935 | | 6.3903 | 27.69 | 71500 | 6.3756 | | 6.3771 | 27.89 | 72000 | 6.3554 | | 6.3763 | 28.08 | 72500 | 6.3911 | | 6.3576 | 28.27 | 73000 | 6.4059 | | 6.3581 | 28.47 | 73500 | 6.3976 | | 6.3739 | 28.66 | 74000 | 6.3921 | | 6.363 | 28.85 | 74500 | 6.3590 | | 6.3687 | 29.05 | 75000 | 6.3683 | | 6.3788 | 29.24 | 75500 | 6.3915 | | 6.3505 | 29.43 | 76000 | 6.3826 | | 6.3618 | 29.63 | 76500 | 6.3833 | | 6.3287 | 29.82 | 77000 | 6.4055 | | 6.3589 | 30.02 | 77500 | 6.3994 | | 6.3614 | 30.21 | 78000 | 6.3848 | | 6.3729 | 30.4 | 78500 | 6.3550 | | 6.3687 | 30.6 | 79000 | 6.3683 | | 6.3377 | 30.79 | 79500 | 6.3743 | | 6.3188 | 30.98 | 80000 | 6.3113 | | 6.3613 | 31.18 | 80500 | 6.3852 | | 6.3428 | 31.37 | 81000 | 6.3610 | | 6.3541 | 31.56 | 81500 | 6.3848 | | 6.3821 | 31.76 | 82000 | 6.3706 | | 6.3357 | 31.95 | 82500 | 6.3191 | | 6.3408 | 32.15 | 83000 | 6.3357 | | 6.3301 | 32.34 | 83500 | 6.3374 | | 6.3681 | 32.53 | 84000 | 6.3583 | | 6.324 | 32.73 | 84500 | 6.3472 | | 6.3615 | 32.92 | 85000 | 6.3359 | | 6.3382 | 33.11 | 85500 | 6.3664 | | 6.34 | 33.31 | 86000 | 6.3281 | | 6.3504 | 33.5 | 86500 | 6.3688 | | 6.3393 | 33.69 | 87000 | 6.3553 | | 6.3453 | 33.89 | 87500 | 6.3493 | | 6.3293 | 34.08 | 88000 | 6.3315 | | 6.3346 | 34.28 | 88500 | 6.3134 | | 6.3325 | 34.47 | 89000 | 6.3631 | | 6.3497 | 34.66 | 89500 | 6.3380 | | 6.332 | 34.86 | 90000 | 6.3484 | | 6.3224 | 35.05 | 90500 | 6.3602 | | 6.3242 | 35.24 | 91000 | 6.3414 | | 6.3346 | 35.44 | 91500 | 6.3151 | | 6.3547 | 35.63 | 92000 | 6.3499 | | 6.3243 | 35.82 | 92500 | 6.3173 | | 6.3148 | 36.02 | 93000 | 6.3141 | | 6.3202 | 36.21 | 93500 | 6.3358 | | 6.3251 | 36.41 | 94000 | 6.2946 | | 6.3313 | 36.6 | 94500 | 6.3413 | | 6.3077 | 36.79 | 95000 | 6.2959 | | 6.3173 | 36.99 | 95500 | 6.3220 | | 6.3207 | 37.18 | 96000 | 6.3630 | | 6.311 | 37.37 | 96500 | 6.3802 | | 6.3259 | 37.57 | 97000 | 6.3425 | | 6.3269 | 37.76 | 97500 | 6.3407 | | 6.3136 | 37.96 | 98000 | 6.3140 | | 6.3007 | 38.15 | 98500 | 6.3392 | | 6.2911 | 38.34 | 99000 | 6.3874 | | 6.3241 | 38.54 | 99500 | 6.3363 | | 6.3056 | 38.73 | 100000 | 6.3766 | | 6.3138 | 38.92 | 100500 | 6.3147 | | 6.3065 | 39.12 | 101000 | 6.3622 | | 6.3118 | 39.31 | 101500 | 6.3200 | | 6.3009 | 39.5 | 102000 | 6.3316 | | 6.3107 | 39.7 | 102500 | 6.3112 | | 6.2977 | 39.89 | 103000 | 6.3120 | | 314232208c31029254c2aec0a0c3575d |
apache-2.0 | ['generated_from_trainer'] | false | 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: 2.4174 | 15c56b4896f126b3970f839de1816d89 |
openrail | [] | false | Chinese weddings need low cfg, such as 3.5-7. Because the training set only has a head portrait, it can only be stable, Forgive me for not doing well, Suggested fusion model Love Chinese style, thank QQ friends for their long-term help and teaching, thank you again Thanks for teacher screw's training set Note It is recommended to use cute face and beautiful face to stabilize the face Negative add long neck，Use vae with high saturation BY昂扬      | 338ed053dadef408efa4862eb7fc47a2 |
apache-2.0 | ['translation'] | false | fr-it * source group: French * target group: Italian * OPUS readme: [fra-ita](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-ita/README.md) * model: transformer-align * source language(s): fra * target language(s): ita * raw source language(s): fra * raw target language(s): ita * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opusTCv20210807-2021-11-11.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-ita/opusTCv20210807-2021-11-11.zip) * test set translations: [opusTCv20210807-2021-11-11.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-ita/opusTCv20210807-2021-11-11.test.txt) * test set scores: [opusTCv20210807-2021-11-11.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-ita/opusTCv20210807-2021-11-11.eval.txt) | c9224caa8cf8ed76d4df60a8e29e0c78 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: fr-it - source_languages: fra - target_languages: ita - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-ita/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['fr', 'it'] - src_constituents: ('French', {'fra'}) - tgt_constituents: ('Italian', {'ita'}) - src_multilingual: False - tgt_multilingual: False - long_pair: fra-ita - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/fra-ita/opusTCv20210807-2021-11-11.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/fra-ita/opusTCv20210807-2021-11-11.test.txt - src_alpha3: fra - tgt_alpha3: ita - chrF2_score: 0.737 - bleu: 54.8 - src_name: French - tgt_name: Italian - train_date: 2021-11-11 00:00:00 - src_alpha2: fr - tgt_alpha2: it - prefer_old: False - short_pair: fr-it - helsinki_git_sha: 7ab0c987850187e0b10342bfc616cd47c027ba18 - transformers_git_sha: df1f94eb4a18b1a27d27e32040b60a17410d516e - port_machine: LM0-400-22516.local - port_time: 2021-11-11-19:40 | 8a583b7f453a770ba407addaa7c52afc |
mit | [] | false | PyAutoCode: GPT-2 based Python auto-code.
PyAutoCode is a cut-down python autosuggestion built on **GPT-2** *(motivation: GPyT)* model. This baby model *(trained only up to 3 epochs)* is not **"fine-tuned"** yet therefore, I highly recommend not to use it in a production environment or incorporate PyAutoCode in any of your projects. It has been trained on **112GB** of Python data sourced from the best crowdsource platform ever -- **GitHub**.
*NOTE: Increased training and fine tuning would be highly appreciated and I firmly believe that it would improve the ability of PyAutoCode significantly.*
| e0b2c995ae715a3bb0f6850b3d2fe094 |
mit | [] | false | Some Model Features
- Built on *GPT-2*
- Tokenized with *ByteLevelBPETokenizer*
- Data Sourced from *GitHub (almost 5 consecutive days of latest Python repositories)*
- Makes use of *GPTLMHeadModel* and *DataCollatorForLanguageModelling* for training
- Newline characters are custom coded as `<N>`
| 206411fcaa790ebb2e3334ac02852a2f |
mit | [] | false | Get a Glimpse of the Model
You can make use of the **Inference API** of huggingface *(present on the right sidebar)* to load the model and check the result. Just enter any code snippet as input. Something like:
```sh
for i in range(
```
| 237be155bdc66115cebfb51e537727f1 |
mit | [] | false | Usage
You can use my model too!. Here's a quick tour of how you can achieve this:
Install transformers
```sh
$ pip install transformers
```
Call the API and get it to work!
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("P0intMaN/PyAutoCode")
model = AutoModelForCausalLM.from_pretrained("P0intMaN/PyAutoCode")
| 9e65ce98571c6a7c9d9e6b0585f55678 |
mit | [] | false | input: single line or multi-line. Highly recommended to use doc-strings.
inp = """import pandas"""
format_inp = inp.replace('\n', "<N>")
tokenize_inp = tokenizer.encode(format_inp, return_tensors='pt')
result = model.generate(tokenize_inp)
decode_result = tokenizer.decode(result[0])
format_result = decode_result.replace('<N>', "\n")
| 5276c90e5f14bb8cb4f6c3afed54e224 |
mit | [] | false | printing the result
print(format_result)
```
Upon successful execution, the above should probably produce *(your results may vary when this model is fine-tuned)*
```sh
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
```
| 7d7dd434b4ff36c754e6bcbf4c50d00a |
mit | ['generated_from_keras_callback'] | false | XLM-roberta-finetuned This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: | 54b221d0eb1472f165a8c4d7d9911aea |
apache-2.0 | ['generated_from_keras_callback'] | false | eduardopds/distilbert-base-uncased-tweets This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7428 - Validation Loss: 0.9322 - Epoch: 9 | 423dd1ca66b7fcf985e13ded56b4c429 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 310, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | c1685bb14f4924206280436628a7744c |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.0162 | 1.0010 | 0 | | 0.9552 | 0.9574 | 1 | | 0.8928 | 0.9393 | 2 | | 0.8238 | 0.9412 | 3 | | 0.7581 | 0.9322 | 4 | | 0.7268 | 0.9322 | 5 | | 0.7310 | 0.9322 | 6 | | 0.7390 | 0.9322 | 7 | | 0.7423 | 0.9322 | 8 | | 0.7428 | 0.9322 | 9 | | 5a40a49f0163f81bde1807ede9c6fc71 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3187 - Accuracy: 0.8633 - F1: 0.8629 | 798b4107dfd88b5de41ce7a45baf9968 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-cola This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan | cf0c08fe1856fc21372c781bf43ff2d5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.0589 | 0.47 | 500 | 2.8255 | | 2.8708 | 0.94 | 1000 | 2.8047 | | 2.7086 | 1.4 | 1500 | 2.6590 | | 2.6021 | 1.87 | 2000 | 2.7510 | | 2.4549 | 2.34 | 2500 | 2.8776 | | 2.4864 | 2.81 | 3000 | nan | | 3ff492d644db351c54be4e34c9f7bb07 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-multilingual-cased-finetuned-pos This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1736 - Precision: 0.9499 - Recall: 0.9504 - F1: 0.9501 - Accuracy: 0.9551 | f76a92caa93377332ae32e4cc8acd140 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.7663 | 0.27 | 200 | 0.2047 | 0.9318 | 0.9312 | 0.9315 | 0.9388 | | 0.5539 | 0.53 | 400 | 0.1815 | 0.9381 | 0.9404 | 0.9392 | 0.9460 | | 0.5222 | 0.8 | 600 | 0.1787 | 0.9400 | 0.9424 | 0.9412 | 0.9468 | | 0.5084 | 1.07 | 800 | 0.1591 | 0.9470 | 0.9463 | 0.9467 | 0.9519 | | 0.4703 | 1.33 | 1000 | 0.1622 | 0.9456 | 0.9458 | 0.9457 | 0.9510 | | 0.5005 | 1.6 | 1200 | 0.1666 | 0.9470 | 0.9464 | 0.9467 | 0.9519 | | 0.4677 | 1.87 | 1400 | 0.1583 | 0.9483 | 0.9483 | 0.9483 | 0.9532 | | 0.4704 | 2.13 | 1600 | 0.1635 | 0.9472 | 0.9475 | 0.9473 | 0.9528 | | 0.4639 | 2.4 | 1800 | 0.1569 | 0.9475 | 0.9488 | 0.9482 | 0.9536 | | 0.4627 | 2.67 | 2000 | 0.1605 | 0.9474 | 0.9478 | 0.9476 | 0.9527 | | 0.4608 | 2.93 | 2200 | 0.1535 | 0.9485 | 0.9495 | 0.9490 | 0.9538 | | 0.4306 | 3.2 | 2400 | 0.1646 | 0.9489 | 0.9487 | 0.9488 | 0.9536 | | 0.4583 | 3.47 | 2600 | 0.1642 | 0.9488 | 0.9495 | 0.9491 | 0.9539 | | 0.453 | 3.73 | 2800 | 0.1646 | 0.9498 | 0.9505 | 0.9501 | 0.9554 | | 0.4347 | 4.0 | 3000 | 0.1629 | 0.9494 | 0.9504 | 0.9499 | 0.9552 | | 0.4425 | 4.27 | 3200 | 0.1738 | 0.9495 | 0.9502 | 0.9498 | 0.9550 | | 0.4335 | 4.53 | 3400 | 0.1733 | 0.9499 | 0.9506 | 0.9503 | 0.9550 | | 0.4306 | 4.8 | 3600 | 0.1736 | 0.9499 | 0.9504 | 0.9501 | 0.9551 | | d5ff05c78a6e5365b1699bddde54acf0 |
mit | ['generated_from_trainer'] | false | roberta-base-mnli This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3617 - Accuracy: 0.8657 | 9ee80c195dab5db8033c119a54559c6a |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10.0 | 092d4250a70759f9a680aaf09f8201d3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 1.0993 | 0.02 | 500 | 1.0983 | 0.3321 | | 1.099 | 0.04 | 1000 | 1.0932 | 0.4276 | | 1.011 | 0.06 | 1500 | 0.8352 | 0.6732 | | 0.7551 | 0.08 | 2000 | 0.6018 | 0.7615 | | 0.6343 | 0.1 | 2500 | 0.5726 | 0.7813 | | 0.5884 | 0.12 | 3000 | 0.5349 | 0.7926 | | 0.5548 | 0.14 | 3500 | 0.4925 | 0.8078 | | 0.5244 | 0.16 | 4000 | 0.4806 | 0.8161 | | 0.5198 | 0.18 | 4500 | 0.4614 | 0.8257 | | 0.5168 | 0.2 | 5000 | 0.4713 | 0.8177 | | 0.5194 | 0.22 | 5500 | 0.4344 | 0.8323 | | 0.485 | 0.24 | 6000 | 0.4527 | 0.8316 | | 0.4909 | 0.26 | 6500 | 0.4377 | 0.8376 | | 0.49 | 0.29 | 7000 | 0.4649 | 0.8266 | | 0.4897 | 0.31 | 7500 | 0.4162 | 0.8413 | | 0.4672 | 0.33 | 8000 | 0.4163 | 0.8425 | | 0.4699 | 0.35 | 8500 | 0.4060 | 0.8451 | | 0.4729 | 0.37 | 9000 | 0.4412 | 0.8387 | | 0.4733 | 0.39 | 9500 | 0.4353 | 0.8401 | | 0.4699 | 0.41 | 10000 | 0.4060 | 0.8476 | | 0.4759 | 0.43 | 10500 | 0.4226 | 0.8358 | | 0.461 | 0.45 | 11000 | 0.4220 | 0.8423 | | 0.4608 | 0.47 | 11500 | 0.4404 | 0.8319 | | 0.462 | 0.49 | 12000 | 0.4280 | 0.8455 | | 0.4533 | 0.51 | 12500 | 0.4128 | 0.8468 | | 0.4691 | 0.53 | 13000 | 0.4155 | 0.8437 | | 0.4552 | 0.55 | 13500 | 0.4385 | 0.8348 | | 0.4573 | 0.57 | 14000 | 0.4498 | 0.8424 | | 0.4562 | 0.59 | 14500 | 0.4162 | 0.8442 | | 0.4665 | 0.61 | 15000 | 0.4417 | 0.8432 | | 0.4569 | 0.63 | 15500 | 0.4113 | 0.8492 | | 0.4705 | 0.65 | 16000 | 0.4454 | 0.8399 | | 0.4685 | 0.67 | 16500 | 0.4055 | 0.8451 | | 0.4475 | 0.69 | 17000 | 0.4426 | 0.8383 | | 0.4641 | 0.71 | 17500 | 0.4256 | 0.8471 | | 0.4299 | 0.73 | 18000 | 0.4260 | 0.8478 | | 0.4439 | 0.75 | 18500 | 0.4218 | 0.8454 | | 0.4628 | 0.77 | 19000 | 0.4087 | 0.8479 | | 0.4502 | 0.79 | 19500 | 0.4238 | 0.8450 | | 0.4299 | 0.81 | 20000 | 0.4091 | 0.8485 | | 0.4496 | 0.84 | 20500 | 0.4160 | 0.8439 | | 0.4492 | 0.86 | 21000 | 0.4109 | 0.8469 | | 0.432 | 0.88 | 21500 | 0.4499 | 0.8493 | | 0.4343 | 0.9 | 22000 | 0.4136 | 0.8465 | | 0.4445 | 0.92 | 22500 | 0.4095 | 0.8433 | | 0.4378 | 0.94 | 23000 | 0.3999 | 0.8483 | | 0.4367 | 0.96 | 23500 | 0.3962 | 0.8509 | | 0.4428 | 0.98 | 24000 | 0.3958 | 0.8504 | | 0.4356 | 1.0 | 24500 | 0.3998 | 0.8558 | | 0.3715 | 1.02 | 25000 | 0.4016 | 0.8589 | | 0.3649 | 1.04 | 25500 | 0.4368 | 0.8582 | | 0.3565 | 1.06 | 26000 | 0.4084 | 0.8519 | | 0.3626 | 1.08 | 26500 | 0.4302 | 0.8438 | | 0.3535 | 1.1 | 27000 | 0.4206 | 0.8557 | | 0.3684 | 1.12 | 27500 | 0.4117 | 0.8561 | | 0.3649 | 1.14 | 28000 | 0.4300 | 0.8527 | | 0.3791 | 1.16 | 28500 | 0.3916 | 0.8585 | | 0.366 | 1.18 | 29000 | 0.4101 | 0.8592 | | 0.3777 | 1.2 | 29500 | 0.3946 | 0.8561 | | 0.3672 | 1.22 | 30000 | 0.4417 | 0.8530 | | 0.3688 | 1.24 | 30500 | 0.4066 | 0.8523 | | 0.3525 | 1.26 | 31000 | 0.4299 | 0.8581 | | 0.3688 | 1.28 | 31500 | 0.3870 | 0.8553 | | 0.3699 | 1.3 | 32000 | 0.3781 | 0.8627 | | 0.3547 | 1.32 | 32500 | 0.4311 | 0.8526 | | 0.3653 | 1.34 | 33000 | 0.4034 | 0.8603 | | 0.3738 | 1.36 | 33500 | 0.4103 | 0.8554 | | 0.3824 | 1.39 | 34000 | 0.3719 | 0.8618 | | 0.3591 | 1.41 | 34500 | 0.4244 | 0.8615 | | 0.3697 | 1.43 | 35000 | 0.4689 | 0.8451 | | 0.3598 | 1.45 | 35500 | 0.4149 | 0.8532 | | 0.3586 | 1.47 | 36000 | 0.4070 | 0.8591 | | 0.3519 | 1.49 | 36500 | 0.4133 | 0.8545 | | 0.3681 | 1.51 | 37000 | 0.3889 | 0.8601 | | 0.3611 | 1.53 | 37500 | 0.3934 | 0.8591 | | 0.3696 | 1.55 | 38000 | 0.4313 | 0.8552 | | 0.3798 | 1.57 | 38500 | 0.3784 | 0.8602 | | 0.3601 | 1.59 | 39000 | 0.3994 | 0.8600 | | 0.3696 | 1.61 | 39500 | 0.4206 | 0.8577 | | 0.368 | 1.63 | 40000 | 0.3903 | 0.8627 | | 0.3473 | 1.65 | 40500 | 0.3813 | 0.8655 | | 0.3604 | 1.67 | 41000 | 0.3930 | 0.8551 | | 0.3741 | 1.69 | 41500 | 0.3644 | 0.8618 | | 0.3551 | 1.71 | 42000 | 0.3936 | 0.8583 | | 0.378 | 1.73 | 42500 | 0.3826 | 0.8607 | | 0.3609 | 1.75 | 43000 | 0.3815 | 0.8618 | | 0.3678 | 1.77 | 43500 | 0.3961 | 0.8578 | | 0.3633 | 1.79 | 44000 | 0.4011 | 0.8603 | | 0.3792 | 1.81 | 44500 | 0.4061 | 0.8592 | | 0.3675 | 1.83 | 45000 | 0.4155 | 0.8631 | | 0.3576 | 1.85 | 45500 | 0.4061 | 0.8589 | | 0.3546 | 1.87 | 46000 | 0.3862 | 0.8623 | | 0.3564 | 1.89 | 46500 | 0.3937 | 0.8607 | | 0.3602 | 1.91 | 47000 | 0.3851 | 0.8646 | | 0.3494 | 1.94 | 47500 | 0.4015 | 0.8541 | | 0.3499 | 1.96 | 48000 | 0.4266 | 0.8545 | | 0.3672 | 1.98 | 48500 | 0.3761 | 0.8588 | | 0.3661 | 2.0 | 49000 | 0.4121 | 0.8567 | | 0.2759 | 2.02 | 49500 | 0.4653 | 0.8645 | | 0.2927 | 2.04 | 50000 | 0.4652 | 0.8597 | | 0.2736 | 2.06 | 50500 | 0.4547 | 0.8597 | | 0.2749 | 2.08 | 51000 | 0.4896 | 0.8565 | | 0.2757 | 2.1 | 51500 | 0.4814 | 0.8639 | | 0.2833 | 2.12 | 52000 | 0.4110 | 0.8656 | | 0.2797 | 2.14 | 52500 | 0.4316 | 0.8636 | | 0.2643 | 2.16 | 53000 | 0.4317 | 0.8599 | | 0.2791 | 2.18 | 53500 | 0.4557 | 0.8617 | | 0.2737 | 2.2 | 54000 | 0.4102 | 0.8624 | | 0.2748 | 2.22 | 54500 | 0.4187 | 0.8585 | | 0.2619 | 2.24 | 55000 | 0.4412 | 0.8590 | | 0.2718 | 2.26 | 55500 | 0.4707 | 0.8618 | | 0.2662 | 2.28 | 56000 | 0.4754 | 0.8594 | | 0.282 | 2.3 | 56500 | 0.4376 | 0.8617 | | 0.284 | 2.32 | 57000 | 0.4393 | 0.8599 | | 0.2733 | 2.34 | 57500 | 0.4531 | 0.8581 | | 0.2878 | 2.36 | 58000 | 0.4727 | 0.8549 | | 0.2812 | 2.38 | 58500 | 0.4221 | 0.8625 | | 0.2657 | 2.4 | 59000 | 0.4456 | 0.8583 | | 0.2716 | 2.42 | 59500 | 0.4455 | 0.8668 | | 0.2766 | 2.44 | 60000 | 0.4940 | 0.8580 | | 0.2871 | 2.46 | 60500 | 0.4460 | 0.8501 | | 0.2731 | 2.49 | 61000 | 0.4600 | 0.8631 | | 0.2885 | 2.51 | 61500 | 0.4229 | 0.8645 | | 0.2764 | 2.53 | 62000 | 0.4107 | 0.8638 | | 0.2866 | 2.55 | 62500 | 0.4250 | 0.8638 | | 0.2754 | 2.57 | 63000 | 0.4846 | 0.8580 | | 0.3028 | 2.59 | 63500 | 0.4339 | 0.8627 | | 0.2828 | 2.61 | 64000 | 0.4697 | 0.8613 | | 0.2875 | 2.63 | 64500 | 0.4167 | 0.8638 | | 0.2836 | 2.65 | 65000 | 0.5050 | 0.8600 | | 0.2978 | 2.67 | 65500 | 0.4139 | 0.8628 | | 0.2946 | 2.69 | 66000 | 0.4449 | 0.8644 | | 0.2822 | 2.71 | 66500 | 0.4302 | 0.8612 | | 0.3006 | 2.73 | 67000 | 0.4256 | 0.8631 | | 0.2896 | 2.75 | 67500 | 0.4993 | 0.8603 | | 0.2787 | 2.77 | 68000 | 0.4467 | 0.8636 | | 0.3 | 2.79 | 68500 | 0.4196 | 0.8592 | | 0.2939 | 2.81 | 69000 | 0.4234 | 0.8614 | | 0.2841 | 2.83 | 69500 | 0.4173 | 0.8660 | | 0.2935 | 2.85 | 70000 | 0.4054 | 0.8658 | | 0.2977 | 2.87 | 70500 | 0.4400 | 0.8623 | | 0.2853 | 2.89 | 71000 | 0.4322 | 0.8668 | | 0.2779 | 2.91 | 71500 | 0.4460 | 0.8595 | | 0.2923 | 2.93 | 72000 | 0.4279 | 0.8619 | | 0.2915 | 2.95 | 72500 | 0.4324 | 0.8625 | | 0.2927 | 2.97 | 73000 | 0.4108 | 0.8672 | | 0.29 | 2.99 | 73500 | 0.4299 | 0.8579 | | 0.2255 | 3.01 | 74000 | 0.5337 | 0.8637 | | 0.2113 | 3.04 | 74500 | 0.5046 | 0.8624 | | 0.207 | 3.06 | 75000 | 0.6011 | 0.8551 | | 0.2226 | 3.08 | 75500 | 0.5426 | 0.8579 | | 0.2129 | 3.1 | 76000 | 0.5036 | 0.8640 | | 0.2201 | 3.12 | 76500 | 0.5629 | 0.8604 | | 0.2185 | 3.14 | 77000 | 0.5416 | 0.8607 | | 0.21 | 3.16 | 77500 | 0.5457 | 0.8605 | | 0.2372 | 3.18 | 78000 | 0.5337 | 0.8594 | | 0.2237 | 3.2 | 78500 | 0.5060 | 0.8679 | | 0.2277 | 3.22 | 79000 | 0.5647 | 0.8651 | | 0.2301 | 3.24 | 79500 | 0.4906 | 0.8602 | | 0.2238 | 3.26 | 80000 | 0.5231 | 0.8647 | | 0.2365 | 3.28 | 80500 | 0.5628 | 0.8621 | | 0.2189 | 3.3 | 81000 | 0.5496 | 0.8630 | | 0.2233 | 3.32 | 81500 | 0.5418 | 0.8639 | | 0.2216 | 3.34 | 82000 | 0.5032 | 0.8689 | | 0.2314 | 3.36 | 82500 | 0.5437 | 0.8634 | | 0.2351 | 3.38 | 83000 | 0.4863 | 0.8653 | | 0.2378 | 3.4 | 83500 | 0.5158 | 0.8635 | | 0.2357 | 3.42 | 84000 | 0.5142 | 0.8629 | | 0.2484 | 3.44 | 84500 | 0.4536 | 0.8657 | | 0.2261 | 3.46 | 85000 | 0.5619 | 0.8649 | | 0.2323 | 3.48 | 85500 | 0.5371 | 0.8587 | | 0.2336 | 3.5 | 86000 | 0.5562 | 0.8621 | | 0.2259 | 3.52 | 86500 | 0.5339 | 0.8589 | | 0.2371 | 3.54 | 87000 | 0.4711 | 0.8665 | | 0.227 | 3.57 | 87500 | 0.5350 | 0.8644 | | 0.2417 | 3.59 | 88000 | 0.4692 | 0.8665 | | 0.2176 | 3.61 | 88500 | 0.5195 | 0.8655 | | 0.2393 | 3.63 | 89000 | 0.5468 | 0.8588 | | 0.2219 | 3.65 | 89500 | 0.5498 | 0.8646 | | 0.23 | 3.67 | 90000 | 0.5367 | 0.8703 | | 0.2317 | 3.69 | 90500 | 0.4761 | 0.8639 | | 0.2241 | 3.71 | 91000 | 0.4992 | 0.8654 | | 0.2327 | 3.73 | 91500 | 0.5040 | 0.8678 | | 0.2312 | 3.75 | 92000 | 0.4943 | 0.8639 | | 0.2369 | 3.77 | 92500 | 0.4824 | 0.8721 | | 0.2235 | 3.79 | 93000 | 0.5090 | 0.8661 | | 0.2256 | 3.81 | 93500 | 0.5258 | 0.8644 | | 0.236 | 3.83 | 94000 | 0.5490 | 0.8542 | | 0.2313 | 3.85 | 94500 | 0.4672 | 0.8677 | | 0.228 | 3.87 | 95000 | 0.5037 | 0.8623 | | 0.2297 | 3.89 | 95500 | 0.5207 | 0.8545 | | 0.2332 | 3.91 | 96000 | 0.5139 | 0.8698 | | 0.2331 | 3.93 | 96500 | 0.5182 | 0.8615 | | 0.2354 | 3.95 | 97000 | 0.5090 | 0.8657 | | 0.2273 | 3.97 | 97500 | 0.5523 | 0.8637 | | 0.2433 | 3.99 | 98000 | 0.5148 | 0.8691 | | 0.191 | 4.01 | 98500 | 0.6007 | 0.8654 | | 0.1683 | 4.03 | 99000 | 0.6770 | 0.8636 | | 0.1778 | 4.05 | 99500 | 0.6595 | 0.8635 | | 0.1832 | 4.07 | 100000 | 0.6129 | 0.8608 | | 0.1842 | 4.09 | 100500 | 0.6612 | 0.8611 | | 0.1865 | 4.12 | 101000 | 0.6551 | 0.8658 | | 0.1833 | 4.14 | 101500 | 0.6294 | 0.8643 | | 0.1869 | 4.16 | 102000 | 0.6234 | 0.8614 | | 0.1806 | 4.18 | 102500 | 0.6417 | 0.8655 | | 0.1911 | 4.2 | 103000 | 0.6426 | 0.8607 | | 0.1981 | 4.22 | 103500 | 0.6247 | 0.8589 | | 0.1731 | 4.24 | 104000 | 0.6613 | 0.8626 | | 0.1977 | 4.26 | 104500 | 0.5441 | 0.8661 | | 0.1771 | 4.28 | 105000 | 0.6608 | 0.8644 | | 0.1903 | 4.3 | 105500 | 0.6174 | 0.8603 | | 0.1797 | 4.32 | 106000 | 0.6609 | 0.8607 | | 0.188 | 4.34 | 106500 | 0.6059 | 0.8643 | | 0.1863 | 4.36 | 107000 | 0.5723 | 0.8663 | | 0.19 | 4.38 | 107500 | 0.5959 | 0.8652 | | 0.1869 | 4.4 | 108000 | 0.5898 | 0.8698 | | 0.1909 | 4.42 | 108500 | 0.6052 | 0.8659 | | 0.1908 | 4.44 | 109000 | 0.5854 | 0.8690 | | 0.203 | 4.46 | 109500 | 0.5727 | 0.8694 | | 0.1993 | 4.48 | 110000 | 0.5877 | 0.8653 | | 0.1796 | 4.5 | 110500 | 0.6231 | 0.8679 | | 0.1837 | 4.52 | 111000 | 0.5749 | 0.8694 | | 0.1885 | 4.54 | 111500 | 0.6174 | 0.8618 | | 0.1902 | 4.56 | 112000 | 0.5625 | 0.8682 | | 0.2031 | 4.58 | 112500 | 0.6252 | 0.8577 | | 0.1986 | 4.6 | 113000 | 0.6147 | 0.8548 | | 0.1769 | 4.62 | 113500 | 0.6351 | 0.8648 | | 0.1974 | 4.64 | 114000 | 0.6396 | 0.8630 | | 0.1952 | 4.67 | 114500 | 0.6174 | 0.8661 | | 0.1904 | 4.69 | 115000 | 0.6188 | 0.8663 | | 0.191 | 4.71 | 115500 | 0.5860 | 0.8646 | | 0.1869 | 4.73 | 116000 | 0.5978 | 0.8586 | | 0.2056 | 4.75 | 116500 | 0.5985 | 0.8648 | | 0.1837 | 4.77 | 117000 | 0.5742 | 0.8636 | | 0.2038 | 4.79 | 117500 | 0.5726 | 0.8662 | | 0.1939 | 4.81 | 118000 | 0.6097 | 0.8623 | | 0.1869 | 4.83 | 118500 | 0.5820 | 0.8651 | | 0.1897 | 4.85 | 119000 | 0.5766 | 0.8666 | | 0.1792 | 4.87 | 119500 | 0.6093 | 0.8683 | | 0.2056 | 4.89 | 120000 | 0.5890 | 0.8633 | | 0.1989 | 4.91 | 120500 | 0.5825 | 0.8674 | | 0.1916 | 4.93 | 121000 | 0.6250 | 0.8641 | | 0.197 | 4.95 | 121500 | 0.5848 | 0.8645 | | 0.1923 | 4.97 | 122000 | 0.5666 | 0.8667 | | 0.1916 | 4.99 | 122500 | 0.6189 | 0.8638 | | 0.1642 | 5.01 | 123000 | 0.7094 | 0.8610 | | 0.1357 | 5.03 | 123500 | 0.6972 | 0.8658 | | 0.1476 | 5.05 | 124000 | 0.6965 | 0.8664 | | 0.1476 | 5.07 | 124500 | 0.7177 | 0.8638 | | 0.1486 | 5.09 | 125000 | 0.6945 | 0.8620 | | 0.1309 | 5.11 | 125500 | 0.7326 | 0.8626 | | 0.1575 | 5.13 | 126000 | 0.6473 | 0.8632 | | 0.1411 | 5.15 | 126500 | 0.6955 | 0.8651 | | 0.1473 | 5.17 | 127000 | 0.6926 | 0.8648 | | 0.153 | 5.19 | 127500 | 0.7010 | 0.8638 | | 0.1488 | 5.22 | 128000 | 0.6643 | 0.8689 | | 0.144 | 5.24 | 128500 | 0.6868 | 0.8668 | | 0.156 | 5.26 | 129000 | 0.6682 | 0.8645 | | 0.1537 | 5.28 | 129500 | 0.6740 | 0.8610 | | 0.1424 | 5.3 | 130000 | 0.7509 | 0.8603 | | 0.1531 | 5.32 | 130500 | 0.6966 | 0.8670 | | 0.1457 | 5.34 | 131000 | 0.7227 | 0.8632 | | 0.1494 | 5.36 | 131500 | 0.6911 | 0.8626 | | 0.1476 | 5.38 | 132000 | 0.6903 | 0.8630 | | 0.1531 | 5.4 | 132500 | 0.6839 | 0.8675 | | 0.1613 | 5.42 | 133000 | 0.6559 | 0.8601 | | 0.1456 | 5.44 | 133500 | 0.7161 | 0.8619 | | 0.1539 | 5.46 | 134000 | 0.7108 | 0.8638 | | 0.1685 | 5.48 | 134500 | 0.6703 | 0.8628 | | 0.1482 | 5.5 | 135000 | 0.6692 | 0.8651 | | 0.1587 | 5.52 | 135500 | 0.6936 | 0.8658 | | 0.152 | 5.54 | 136000 | 0.6844 | 0.8661 | | 0.1619 | 5.56 | 136500 | 0.6632 | 0.8641 | | 0.154 | 5.58 | 137000 | 0.6451 | 0.8666 | | 0.1525 | 5.6 | 137500 | 0.6529 | 0.8686 | | 0.1545 | 5.62 | 138000 | 0.6860 | 0.8603 | | 0.1487 | 5.64 | 138500 | 0.6842 | 0.8668 | | 0.1546 | 5.66 | 139000 | 0.6692 | 0.8655 | | 0.168 | 5.68 | 139500 | 0.6701 | 0.8649 | | 0.1513 | 5.7 | 140000 | 0.6613 | 0.8680 | | 0.1704 | 5.72 | 140500 | 0.6804 | 0.8643 | | 0.1517 | 5.74 | 141000 | 0.6871 | 0.8684 | | 0.1572 | 5.77 | 141500 | 0.6676 | 0.8670 | | 0.1551 | 5.79 | 142000 | 0.6919 | 0.8638 | | 0.1483 | 5.81 | 142500 | 0.6801 | 0.8667 | | 0.1562 | 5.83 | 143000 | 0.6791 | 0.8628 | | 0.1594 | 5.85 | 143500 | 0.6422 | 0.8671 | | 0.1627 | 5.87 | 144000 | 0.6526 | 0.8679 | | 0.1514 | 5.89 | 144500 | 0.6734 | 0.8698 | | 0.1546 | 5.91 | 145000 | 0.6377 | 0.8711 | | 0.146 | 5.93 | 145500 | 0.7214 | 0.8657 | | 0.1608 | 5.95 | 146000 | 0.6756 | 0.8674 | | 0.1648 | 5.97 | 146500 | 0.6387 | 0.8687 | | 0.1547 | 5.99 | 147000 | 0.6871 | 0.8646 | | 0.1304 | 6.01 | 147500 | 0.7543 | 0.8633 | | 0.1059 | 6.03 | 148000 | 0.7576 | 0.8638 | | 0.1089 | 6.05 | 148500 | 0.7530 | 0.8642 | | 0.112 | 6.07 | 149000 | 0.7951 | 0.8640 | | 0.1198 | 6.09 | 149500 | 0.7381 | 0.8636 | | 0.1222 | 6.11 | 150000 | 0.7560 | 0.8623 | | 0.1024 | 6.13 | 150500 | 0.7965 | 0.8669 | | 0.125 | 6.15 | 151000 | 0.7613 | 0.8620 | | 0.1005 | 6.17 | 151500 | 0.7851 | 0.8651 | | 0.1196 | 6.19 | 152000 | 0.7637 | 0.8652 | | 0.1133 | 6.21 | 152500 | 0.7810 | 0.8660 | | 0.1271 | 6.23 | 153000 | 0.7510 | 0.8672 | | 0.1167 | 6.25 | 153500 | 0.7670 | 0.8638 | | 0.1198 | 6.27 | 154000 | 0.7770 | 0.8632 | | 0.1194 | 6.29 | 154500 | 0.7720 | 0.8607 | | 0.1215 | 6.32 | 155000 | 0.7880 | 0.8609 | | 0.1134 | 6.34 | 155500 | 0.8026 | 0.8617 | | 0.1113 | 6.36 | 156000 | 0.7632 | 0.8652 | | 0.1207 | 6.38 | 156500 | 0.7369 | 0.8686 | | 0.1188 | 6.4 | 157000 | 0.7466 | 0.8657 | | 0.1283 | 6.42 | 157500 | 0.7531 | 0.8645 | | 0.1186 | 6.44 | 158000 | 0.7529 | 0.8673 | | 0.135 | 6.46 | 158500 | 0.7706 | 0.8589 | | 0.1116 | 6.48 | 159000 | 0.7754 | 0.8646 | | 0.1295 | 6.5 | 159500 | 0.7026 | 0.8693 | | 0.1309 | 6.52 | 160000 | 0.7342 | 0.8656 | | 0.1172 | 6.54 | 160500 | 0.7828 | 0.8644 | | 0.125 | 6.56 | 161000 | 0.7456 | 0.8671 | | 0.1199 | 6.58 | 161500 | 0.7464 | 0.8701 | | 0.1197 | 6.6 | 162000 | 0.7626 | 0.8639 | | 0.1126 | 6.62 | 162500 | 0.8115 | 0.8609 | | 0.1365 | 6.64 | 163000 | 0.7407 | 0.8681 | | 0.122 | 6.66 | 163500 | 0.7648 | 0.8641 | | 0.1157 | 6.68 | 164000 | 0.7636 | 0.8669 | | 0.118 | 6.7 | 164500 | 0.7688 | 0.8686 | | 0.1173 | 6.72 | 165000 | 0.8051 | 0.8687 | | 0.1137 | 6.74 | 165500 | 0.8101 | 0.8635 | | 0.1412 | 6.76 | 166000 | 0.7004 | 0.8689 | | 0.1131 | 6.78 | 166500 | 0.7589 | 0.8664 | | 0.1232 | 6.8 | 167000 | 0.7657 | 0.8654 | | 0.1343 | 6.82 | 167500 | 0.7547 | 0.8652 | | 0.1208 | 6.84 | 168000 | 0.7407 | 0.8699 | | 0.1284 | 6.87 | 168500 | 0.7182 | 0.8677 | | 0.1182 | 6.89 | 169000 | 0.7248 | 0.8681 | | 0.1166 | 6.91 | 169500 | 0.7385 | 0.8678 | | 0.1289 | 6.93 | 170000 | 0.7293 | 0.8672 | | 0.1243 | 6.95 | 170500 | 0.7178 | 0.8696 | | 0.1256 | 6.97 | 171000 | 0.7291 | 0.8633 | | 0.1162 | 6.99 | 171500 | 0.7515 | 0.8648 | | 0.1013 | 7.01 | 172000 | 0.7824 | 0.8655 | | 0.0811 | 7.03 | 172500 | 0.8297 | 0.8647 | | 0.0831 | 7.05 | 173000 | 0.8144 | 0.8678 | | 0.0872 | 7.07 | 173500 | 0.8176 | 0.8679 | | 0.0868 | 7.09 | 174000 | 0.8405 | 0.8642 | | 0.0756 | 7.11 | 174500 | 0.8867 | 0.8642 | | 0.0882 | 7.13 | 175000 | 0.8185 | 0.8659 | | 0.0879 | 7.15 | 175500 | 0.8653 | 0.8625 | | 0.0831 | 7.17 | 176000 | 0.8323 | 0.8655 | | 0.0847 | 7.19 | 176500 | 0.8358 | 0.8650 | | 0.0938 | 7.21 | 177000 | 0.7967 | 0.8665 | | 0.0908 | 7.23 | 177500 | 0.8147 | 0.8640 | | 0.0809 | 7.25 | 178000 | 0.8325 | 0.8679 | | 0.0993 | 7.27 | 178500 | 0.8131 | 0.8655 | | 0.087 | 7.29 | 179000 | 0.8249 | 0.8628 | | 0.0873 | 7.31 | 179500 | 0.8326 | 0.8661 | | 0.0889 | 7.33 | 180000 | 0.8171 | 0.8685 | | 0.0739 | 7.35 | 180500 | 0.8686 | 0.8642 | | 0.0821 | 7.37 | 181000 | 0.8739 | 0.8669 | | 0.0981 | 7.39 | 181500 | 0.8558 | 0.8639 | | 0.0858 | 7.42 | 182000 | 0.8276 | 0.8673 | | 0.083 | 7.44 | 182500 | 0.8148 | 0.8675 | | 0.0969 | 7.46 | 183000 | 0.8520 | 0.8630 | | 0.0851 | 7.48 | 183500 | 0.8604 | 0.8671 | | 0.0881 | 7.5 | 184000 | 0.8665 | 0.8634 | | 0.1036 | 7.52 | 184500 | 0.8233 | 0.8642 | | 0.0874 | 7.54 | 185000 | 0.8293 | 0.8660 | | 0.0935 | 7.56 | 185500 | 0.8006 | 0.8671 | | 0.0887 | 7.58 | 186000 | 0.8352 | 0.8637 | | 0.0897 | 7.6 | 186500 | 0.8309 | 0.8655 | | 0.0788 | 7.62 | 187000 | 0.8505 | 0.8653 | | 0.0887 | 7.64 | 187500 | 0.8465 | 0.8657 | | 0.0909 | 7.66 | 188000 | 0.8582 | 0.8637 | | 0.0895 | 7.68 | 188500 | 0.8487 | 0.8659 | | 0.0729 | 7.7 | 189000 | 0.8770 | 0.8636 | | 0.0758 | 7.72 | 189500 | 0.8717 | 0.8653 | | 0.0901 | 7.74 | 190000 | 0.8513 | 0.8639 | | 0.0848 | 7.76 | 190500 | 0.8554 | 0.8661 | | 0.0985 | 7.78 | 191000 | 0.8259 | 0.8640 | | 0.091 | 7.8 | 191500 | 0.8483 | 0.8644 | | 0.0868 | 7.82 | 192000 | 0.8776 | 0.8602 | | 0.0898 | 7.84 | 192500 | 0.8470 | 0.8634 | | 0.0959 | 7.86 | 193000 | 0.8344 | 0.8645 | | 0.0939 | 7.88 | 193500 | 0.8419 | 0.8641 | | 0.0769 | 7.9 | 194000 | 0.8355 | 0.8673 | | 0.0808 | 7.92 | 194500 | 0.8642 | 0.8646 | | 0.0797 | 7.94 | 195000 | 0.8401 | 0.8663 | | 0.0875 | 7.97 | 195500 | 0.8598 | 0.8638 | | 0.0896 | 7.99 | 196000 | 0.8624 | 0.8648 | | 0.0762 | 8.01 | 196500 | 0.8645 | 0.8656 | | 0.0552 | 8.03 | 197000 | 0.8844 | 0.8661 | | 0.0598 | 8.05 | 197500 | 0.8870 | 0.8663 | | 0.0528 | 8.07 | 198000 | 0.8866 | 0.8679 | | 0.0679 | 8.09 | 198500 | 0.8835 | 0.8657 | | 0.0628 | 8.11 | 199000 | 0.9017 | 0.8635 | | 0.0644 | 8.13 | 199500 | 0.8979 | 0.8647 | | 0.0446 | 8.15 | 200000 | 0.9144 | 0.8656 | | 0.0524 | 8.17 | 200500 | 0.9116 | 0.8651 | | 0.0561 | 8.19 | 201000 | 0.9281 | 0.8639 | | 0.0525 | 8.21 | 201500 | 0.9115 | 0.8672 | | 0.0646 | 8.23 | 202000 | 0.8933 | 0.8663 | | 0.0691 | 8.25 | 202500 | 0.8591 | 0.8662 | | 0.0708 | 8.27 | 203000 | 0.8525 | 0.8683 | | 0.0598 | 8.29 | 203500 | 0.8663 | 0.8689 | | 0.0513 | 8.31 | 204000 | 0.8671 | 0.8704 | | 0.0564 | 8.33 | 204500 | 0.8597 | 0.8694 | | 0.0619 | 8.35 | 205000 | 0.8645 | 0.8683 | | 0.0563 | 8.37 | 205500 | 0.8848 | 0.8658 | | 0.0615 | 8.39 | 206000 | 0.8728 | 0.8663 | | 0.0668 | 8.41 | 206500 | 0.8925 | 0.8657 | | 0.0592 | 8.43 | 207000 | 0.8644 | 0.8673 | | 0.0668 | 8.45 | 207500 | 0.8601 | 0.8700 | | 0.071 | 8.47 | 208000 | 0.8735 | 0.8682 | | 0.061 | 8.49 | 208500 | 0.8797 | 0.8662 | | 0.0627 | 8.52 | 209000 | 0.8742 | 0.8663 | | 0.0505 | 8.54 | 209500 | 0.9063 | 0.8649 | | 0.0607 | 8.56 | 210000 | 0.8940 | 0.8677 | | 0.0569 | 8.58 | 210500 | 0.8953 | 0.8673 | | 0.0671 | 8.6 | 211000 | 0.8784 | 0.8667 | | 0.0509 | 8.62 | 211500 | 0.8942 | 0.8678 | | 0.0526 | 8.64 | 212000 | 0.8968 | 0.8686 | | 0.0541 | 8.66 | 212500 | 0.8950 | 0.8694 | | 0.0677 | 8.68 | 213000 | 0.8808 | 0.8665 | | 0.0552 | 8.7 | 213500 | 0.8923 | 0.8662 | | 0.053 | 8.72 | 214000 | 0.9118 | 0.8673 | | 0.0608 | 8.74 | 214500 | 0.9023 | 0.8700 | | 0.0573 | 8.76 | 215000 | 0.9096 | 0.8681 | | 0.0621 | 8.78 | 215500 | 0.8872 | 0.8684 | | 0.0559 | 8.8 | 216000 | 0.8837 | 0.8672 | | 0.0593 | 8.82 | 216500 | 0.8937 | 0.8675 | | 0.0633 | 8.84 | 217000 | 0.8746 | 0.8685 | | 0.0548 | 8.86 | 217500 | 0.9049 | 0.8662 | | 0.0427 | 8.88 | 218000 | 0.9195 | 0.8685 | | 0.0623 | 8.9 | 218500 | 0.9146 | 0.8669 | | 0.0594 | 8.92 | 219000 | 0.9096 | 0.8672 | | 0.0683 | 8.94 | 219500 | 0.8778 | 0.8679 | | 0.0659 | 8.96 | 220000 | 0.8552 | 0.8699 | | 0.0603 | 8.98 | 220500 | 0.8901 | 0.8679 | | 0.0566 | 9.0 | 221000 | 0.8997 | 0.8677 | | 0.0443 | 9.02 | 221500 | 0.9009 | 0.8683 | | 0.0358 | 9.04 | 222000 | 0.9193 | 0.8680 | | 0.0317 | 9.07 | 222500 | 0.9319 | 0.8687 | | 0.0384 | 9.09 | 223000 | 0.9155 | 0.8699 | | 0.0432 | 9.11 | 223500 | 0.9243 | 0.8685 | | 0.0408 | 9.13 | 224000 | 0.9251 | 0.8693 | | 0.0443 | 9.15 | 224500 | 0.9322 | 0.8677 | | 0.0438 | 9.17 | 225000 | 0.9371 | 0.8666 | | 0.0379 | 9.19 | 225500 | 0.9283 | 0.8693 | | 0.0411 | 9.21 | 226000 | 0.9147 | 0.8703 | | 0.036 | 9.23 | 226500 | 0.9167 | 0.8703 | | 0.0394 | 9.25 | 227000 | 0.9254 | 0.8688 | | 0.0363 | 9.27 | 227500 | 0.9288 | 0.8704 | | 0.0492 | 9.29 | 228000 | 0.9242 | 0.8693 | | 0.0411 | 9.31 | 228500 | 0.9325 | 0.8677 | | 0.0408 | 9.33 | 229000 | 0.9370 | 0.8690 | | 0.0326 | 9.35 | 229500 | 0.9417 | 0.8705 | | 0.038 | 9.37 | 230000 | 0.9480 | 0.8700 | | 0.0412 | 9.39 | 230500 | 0.9398 | 0.8693 | | 0.0588 | 9.41 | 231000 | 0.9174 | 0.8707 | | 0.0417 | 9.43 | 231500 | 0.9204 | 0.8715 | | 0.0362 | 9.45 | 232000 | 0.9319 | 0.8701 | | 0.0283 | 9.47 | 232500 | 0.9562 | 0.8696 | | 0.0353 | 9.49 | 233000 | 0.9525 | 0.8690 | | 0.0384 | 9.51 | 233500 | 0.9561 | 0.8687 | | 0.0406 | 9.53 | 234000 | 0.9375 | 0.8715 | | 0.0356 | 9.55 | 234500 | 0.9575 | 0.8690 | | 0.044 | 9.57 | 235000 | 0.9429 | 0.8708 | | 0.0444 | 9.6 | 235500 | 0.9413 | 0.8690 | | 0.0421 | 9.62 | 236000 | 0.9412 | 0.8689 | | 0.038 | 9.64 | 236500 | 0.9352 | 0.8695 | | 0.0355 | 9.66 | 237000 | 0.9362 | 0.8689 | | 0.04 | 9.68 | 237500 | 0.9403 | 0.8691 | | 0.0356 | 9.7 | 238000 | 0.9402 | 0.8706 | | 0.0383 | 9.72 | 238500 | 0.9466 | 0.8692 | | 0.0534 | 9.74 | 239000 | 0.9378 | 0.8700 | | 0.0383 | 9.76 | 239500 | 0.9390 | 0.8697 | | 0.0418 | 9.78 | 240000 | 0.9404 | 0.8694 | | 0.0335 | 9.8 | 240500 | 0.9390 | 0.8705 | | 0.0398 | 9.82 | 241000 | 0.9430 | 0.8696 | | 0.0336 | 9.84 | 241500 | 0.9438 | 0.8698 | | 0.045 | 9.86 | 242000 | 0.9414 | 0.8703 | | 0.0401 | 9.88 | 242500 | 0.9425 | 0.8696 | | 0.0454 | 9.9 | 243000 | 0.9405 | 0.8696 | | 0.0361 | 9.92 | 243500 | 0.9394 | 0.8696 | | 0.0458 | 9.94 | 244000 | 0.9400 | 0.8690 | | 0.0329 | 9.96 | 244500 | 0.9402 | 0.8693 | | 0.0469 | 9.98 | 245000 | 0.9401 | 0.8691 | | 28bd884f769b392132129566cab9faab |
creativeml-openrail-m | ['text-to-image'] | false | noda model Dreambooth model trained by tommy19970714 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: noda (use that on your prompt)  | 878e1ccb00c192a7ecec7b687e51228c |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-finetune This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset. It achieves the following results on the evaluation set: - Loss: 972.3115 - Wer: 1.0 | b8c7c0ec552ded0869e4d8803f8a1009 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 60 - mixed_precision_training: Native AMP | f5583fb5fb19e99c57d56ebb863984f4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 3325.1297 | 1.39 | 100 | 4054.7283 | 1.0 | | 1624.4673 | 2.77 | 200 | 1100.8928 | 1.0 | | 1079.3557 | 4.17 | 300 | 1009.5025 | 1.0 | | 1026.4995 | 5.55 | 400 | 979.0 | 1.0 | | 1005.6487 | 6.94 | 500 | 964.3292 | 1.0 | | 1000.4138 | 8.33 | 600 | 972.3115 | 1.0 | | 159ef42476b4ea51eccbb7c310f99d73 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Western Frisian (Netherlands) This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 fy-NL dataset. It achieves the following results on the evaluation set: - Loss: 0.5703 - Wer: 21.8466 | c9f260899a40f5102a79f20fc708692e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0078 | 10.01 | 1000 | 0.5184 | 23.0973 | | 0.0009 | 21.0 | 2000 | 0.5653 | 22.5434 | | 0.0007 | 31.01 | 3000 | 0.5703 | 21.8466 | | 0.0004 | 42.0 | 4000 | 0.5968 | 21.9574 | | 0.0003 | 52.01 | 5000 | 0.6044 | 22.0360 | | 11ace8bcc5684ae501fe4a2c6a81c6f0 |
apache-2.0 | ['generated_from_trainer'] | false | ApacheBertBaseCase This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2008 | 479b89be91ea9e3e12162e39b9dd86a4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.2938 | 1.0 | 20881 | 0.2663 | | 0.2345 | 2.0 | 41762 | 0.2134 | | 0.2182 | 3.0 | 62643 | 0.2008 | | c76bfdcfbda97580a667753b9818d1c0 |
apache-2.0 | ['translation'] | false | eng-afa * source group: English * target group: Afro-Asiatic languages * OPUS readme: [eng-afa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-afa/README.md) * model: transformer * source language(s): eng * target language(s): acm afb amh apc ara arq ary arz hau_Latn heb kab mlt rif_Latn shy_Latn som tir * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-afa/opus2m-2020-08-01.zip) * test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-afa/opus2m-2020-08-01.test.txt) * test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-afa/opus2m-2020-08-01.eval.txt) | 6d1f9a5cb756771a83d44dddc5d02293 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.eng-amh.eng.amh | 11.6 | 0.504 | | Tatoeba-test.eng-ara.eng.ara | 12.0 | 0.404 | | Tatoeba-test.eng-hau.eng.hau | 10.2 | 0.429 | | Tatoeba-test.eng-heb.eng.heb | 32.3 | 0.551 | | Tatoeba-test.eng-kab.eng.kab | 1.6 | 0.191 | | Tatoeba-test.eng-mlt.eng.mlt | 17.7 | 0.551 | | Tatoeba-test.eng.multi | 14.4 | 0.375 | | Tatoeba-test.eng-rif.eng.rif | 1.7 | 0.103 | | Tatoeba-test.eng-shy.eng.shy | 0.8 | 0.090 | | Tatoeba-test.eng-som.eng.som | 16.0 | 0.429 | | Tatoeba-test.eng-tir.eng.tir | 2.7 | 0.238 | | df1db7cd6c81dd476df8088381393969 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-afa - source_languages: eng - target_languages: afa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-afa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'so', 'ti', 'am', 'he', 'mt', 'ar', 'afa'] - src_constituents: {'eng'} - tgt_constituents: {'som', 'rif_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau_Latn', 'acm', 'ary'} - src_multilingual: False - tgt_multilingual: True - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-afa/opus2m-2020-08-01.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-afa/opus2m-2020-08-01.test.txt - src_alpha3: eng - tgt_alpha3: afa - short_pair: en-afa - chrF2_score: 0.375 - bleu: 14.4 - brevity_penalty: 1.0 - ref_len: 58110.0 - src_name: English - tgt_name: Afro-Asiatic languages - train_date: 2020-08-01 - src_alpha2: en - tgt_alpha2: afa - prefer_old: False - long_pair: eng-afa - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 9f1979eb1ccd4398566e16a00db6c9cd |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large Vietnamese - Drishti Sharma This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3681 - Wer: 16.6594 | a4d1bbe8f148d62ecf3c67d57ae0df6a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9.5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 600 - mixed_precision_training: Native AMP | 6c3424c9742fb21fd2bde9a739297665 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0667 | 1.73 | 600 | 0.3681 | 16.6594 | | 1a07d60e46d33b0035642cf18cc14b80 |
mit | ['generated_from_trainer', 'BACnet'] | false | BACnet-Klassifizierung-Gewerke-bert-base-german-cased This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the [gart-labor](https://huggingface.co/gart-labor) "klassifizierung_gewerke" dataset. It achieves the following results on the evaluation set: - Loss: 0.0394 - F1: [0.96296296 0.8 0.97297297 1. 0.99469027 0.98979592 0.98969072] | de0a8fc69d722a66b83444cf78533abe |
mit | ['generated_from_trainer', 'BACnet'] | false | Model description This model makes it possible to classify the components of the technical building equipment described with the BACnet standard into different trades. The model is based on a German-language data set. | e6e615159f572c7a2de59e4785bee6e9 |
mit | ['generated_from_trainer', 'BACnet'] | false | Intended uses & limitations The model divides descriptive texts into the following building services trades: Waste_water_water_gas_systems, Other_systems, Building_automation, Refrigeration_systems, Air_technical_systems, Heavy_current_systems and Heat_supply_systems | fef92f1e2ee26c202be0feeae6fc4331 |
mit | ['generated_from_trainer', 'BACnet'] | false | 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: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 | 26166609a180d0977aed6978002904cb |
mit | ['generated_from_trainer', 'BACnet'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------:| | 0.4309 | 0.99 | 45 | 0.0736 | [0.89655172 0.84210526 0.97297297 0.98901099 0.9929078 0.99492386 0.98701299] | | 0.0722 | 1.99 | 90 | 0.0511 | [0.92307692 0.875 0.96 1. 0.99295775 0.98979592 0.98714653] | | 0.0431 | 2.99 | 135 | 0.0460 | [1. 0.8 0.97297297 1. 0.99469027 0.98979592 0.99224806] | | 0.0313 | 3.99 | 180 | 0.0365 | [1. 0.84210526 0.97297297 1. 0.99646643 0.98979592 0.99224806] | | 0.0238 | 4.99 | 225 | 0.0394 | [0.96296296 0.8 0.97297297 1. 0.99469027 0.98979592 0.98969072] | | c115bc20b181be16a71e67c48e2c7acd |
apache-2.0 | ['generated_from_trainer'] | false | swin-base-finetuned-cifar100 This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the cifar100 dataset. It achieves the following results on the evaluation set: - Accuracy: 0.9201 - Loss: 0.3670 | 3aab9cf149e62c8b730b87ddcdabb018 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - 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 | ac62f93a0620b05a67aa22d696c5c739 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:----:|:--------:|:---------------:| | 0.3536 | 1.0 | 781 | 0.9052 | 0.3141 | | 0.3254 | 2.0 | 1562 | 0.9117 | 0.2991 | | 0.0936 | 3.0 | 2343 | 0.9138 | 0.3322 | | 0.1054 | 4.0 | 3124 | 0.9158 | 0.3483 | | 0.0269 | 5.0 | 3905 | 0.9201 | 0.3670 | | 9f382c1fc95f676fd48fecb1e8b7aef8 |
apache-2.0 | [] | false | This model is used to detect **Offensive Content** in **Malayalam Code-Mixed language**. The mono in the name refers to the monolingual setting, where the model is trained using only Malayalam(pure and code-mixed) data. The weights are initialized from pretrained XLM-Roberta-Base and pretrained using Masked Language Modelling on the target dataset before fine-tuning using Cross-Entropy Loss. This model is the best of multiple trained for **EACL 2021 Shared Task on Offensive Language Identification in Dravidian Languages**. Genetic-Algorithm based ensembled test predictions got the highest weighted F1 score at the leaderboard (Weighted F1 score on hold out test set: This model - 0.97, Ensemble - 0.97) | ed09f5f48c3e64d6638112e1007b2061 |
apache-2.0 | [] | false | For more details about our paper Debjoy Saha, Naman Paharia, Debajit Chakraborty, Punyajoy Saha, Animesh Mukherjee. "[Hate-Alert@DravidianLangTech-EACL2021: Ensembling strategies for Transformer-based Offensive language Detection](https://www.aclweb.org/anthology/2021.dravidianlangtech-1.38/)". ***Please cite our paper in any published work that uses any of these resources.*** ~~~ @inproceedings{saha-etal-2021-hate, title = "Hate-Alert@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensembling strategies for Transformer-based Offensive language Detection", author = "Saha, Debjoy and Paharia, Naman and Chakraborty, Debajit and Saha, Punyajoy and Mukherjee, Animesh", booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages", month = apr, year = "2021", address = "Kyiv", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.dravidianlangtech-1.38", pages = "270--276", abstract = "Social media often acts as breeding grounds for different forms of offensive content. For low resource languages like Tamil, the situation is more complex due to the poor performance of multilingual or language-specific models and lack of proper benchmark datasets. Based on this shared task {``}Offensive Language Identification in Dravidian Languages{''} at EACL 2021; we present an exhaustive exploration of different transformer models, We also provide a genetic algorithm technique for ensembling different models. Our ensembled models trained separately for each language secured the first position in Tamil, the second position in Kannada, and the first position in Malayalam sub-tasks. The models and codes are provided.", } ~~~ | de741bb63efca5bea88f9bff4c5b2b87 |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Medium Lithuanian CV11 This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 lt dataset. It achieves the following results on the evaluation set: - Loss: 0.354951 - Wer: 20.446244 | dd24eb797648f684c3f86ebcf633fb94 |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0056 | 9.42 | 1000 | 0.3252 | 20.5534 | | 0.0023 | 18.8 | 2000 | 0.3549 | 20.4462 | | 1831b1a1adb92ef7ef9fff3a2f9a652b |
mit | [] | false | Model Description This model is based on [bigbird-small-indonesian](https://huggingface.co/ilos-vigil/bigbird-small-indonesian) and was finetuned on 2 datasets. It is intended to be used for zero-shot text classification. | ae06168f9426778dab8249eefe01420b |
mit | [] | false | How to use > Inference for ZSC (Zero Shot Classification) task ```py >>> pipe = pipeline( ... task='zero-shot-classification', ... model='./tmp/checkpoint-28832' ... ) >>> pipe( ... sequences='Fakta nomor 7 akan membuat ada terkejut', ... candidate_labels=['clickbait', 'bukan clickbait'], ... hypothesis_template='Judul video ini {}.', ... multi_label=False ... ) { 'sequence': 'Fakta nomor 7 akan membuat ada terkejut', 'labels': ['clickbait', 'bukan clickbait'], 'scores': [0.6102734804153442, 0.38972654938697815] } >>> pipe( ... sequences='Samsung tuntut balik Apple dengan alasan hak paten teknologi.', ... candidate_labels=['teknologi', 'olahraga', 'bisnis', 'politik', 'kesehatan', 'kuliner'], ... hypothesis_template='Kategori berita ini adalah {}.', ... multi_label=True ... ) { 'sequence': 'Samsung tuntut balik Apple dengan alasan hak paten teknologi.', 'labels': ['politik', 'teknologi', 'kesehatan', 'bisnis', 'olahraga', 'kuliner'], 'scores': [0.7390161752700806, 0.6657379269599915, 0.4459509551525116, 0.38407933712005615, 0.3679264783859253, 0.14181996881961823] } ``` > Inference for NLI (Natural Language Inference) task ```py >>> pipe = pipeline( ... task='text-classification', ... model='./tmp/checkpoint-28832', ... return_all_scores=True ... ) >>> pipe({ ... 'text': 'Nasi adalah makanan pokok.', | 9fdd05bb52bd4404e70ad468d887c24e |
mit | [] | false | Hypothesis ... }) [ {'label': 'entailment', 'score': 0.25495028495788574}, {'label': 'neutral', 'score': 0.40920916199684143}, {'label': 'contradiction', 'score': 0.33584052324295044} ] >>> pipe({ ... 'text': 'Python sering digunakan untuk web development dan AI research.', ... 'text_pair': 'AI research biasanya tidak menggunakan bahasa pemrograman Python.' ... }) [ {'label': 'entailment', 'score': 0.12508109211921692}, {'label': 'neutral', 'score': 0.22146646678447723}, {'label': 'contradiction', 'score': 0.653452455997467} ] ``` | 0d0f27e02fcaa767a867b85ed3ed76ac |
mit | [] | false | Limitation and bias This model inherit limitation/bias from it's parent model and 2 datasets used for fine-tuning. And just like most language model, this model is sensitive towards input change. Here's an example. ```py >>> from transformers import pipeline >>> pipe = pipeline( ... task='zero-shot-classification', ... model='./tmp/checkpoint-28832' ... ) >>> text = 'Resep sate ayam enak dan mudah.' >>> candidate_labels = ['kuliner', 'olahraga'] >>> pipe( ... sequences=text, ... candidate_labels=candidate_labels, ... hypothesis_template='Kategori judul artikel ini adalah {}.', ... multi_label=False ... ) { 'sequence': 'Resep sate ayam enak dan mudah.', 'labels': ['kuliner', 'olahraga'], 'scores': [0.7711364030838013, 0.22886358201503754] } >>> pipe( ... sequences=text, ... candidate_labels=candidate_labels, ... hypothesis_template='Kelas kalimat ini {}.', ... multi_label=False ... ) { 'sequence': 'Resep sate ayam enak dan mudah.', 'labels': ['kuliner', 'olahraga'], 'scores': [0.7043636441230774, 0.295636385679245] } >>> pipe( ... sequences=text, ... candidate_labels=candidate_labels, ... hypothesis_template='{}.', ... multi_label=False ... ) { 'sequence': 'Resep sate ayam enak dan mudah.', 'labels': ['kuliner', 'olahraga'], 'scores': [0.5986711382865906, 0.4013288915157318] } ``` | 27051a1f6c9672a045f7744e89e5f2ea |
mit | [] | false | Training, evaluation and testing data This model was finetuned with [IndoNLI](https://huggingface.co/datasets/indonli) and [multilingual-NLI-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-NLI-26lang-2mil7). Although `multilingual-NLI-26lang-2mil7` dataset is machine-translated, this dataset slightly improve result of NLI benchmark and extensively improve result of ZSC benchmark. Both evaluation and testing data is only based on IndoNLI dataset. | 384431627f06644f192b0caf74b5bff3 |
mit | [] | false | Training Procedure The model was finetuned on single RTX 3060 with 16 epoch/28832 steps with accumulated batch size 64. AdamW optimizer is used with LR 1e-4, weight decay 0.05, learning rate warmup for first 6% steps (1730 steps) and linear decay of the learning rate afterwards. Take note while model weight on epoch 9 has lowest loss/highest accuracy, it has slightly lower performance on ZSC benchmark. Additional information can be seen on Tensorboard training logs. | 959ae5ab460e0538d5fd8c2c2be67e85 |
mit | [] | false | Benchmark as NLI model Both benchmark show result of 2 different model as additional comparison. Additional benchmark using IndoNLI dataset is available on it's paper [IndoNLI: A Natural Language Inference Dataset for Indonesian](https://aclanthology.org/2021.emnlp-main.821/). | Model | bigbird-small-indonesian-nli | xlm-roberta-large-xnli | mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 | | ------------------------------------------ | ---------------------------- | ---------------------- | -------------------------------------------- | | Parameter | 30.6M | 559.9M | 278.8M | | Multilingual | | V | V | | Finetuned on IndoNLI | V | | V | | Finetuned on multilingual-NLI-26lang-2mil7 | V | | | | Test (Lay) | 0.6888 | 0.2226 | 0.8151 | | Test (Expert) | 0.5734 | 0.3505 | 0.7775 | | b57aafd36371b42aa1b9391e5e6eee7b |
mit | [] | false | Benchmark as ZSC model [Indonesian-Twitter-Emotion-Dataset](https://github.com/meisaputri21/Indonesian-Twitter-Emotion-Dataset/) is used to perform ZSC benchmark. This benchmark include 4 different parameter which affect performance of each model differently. Hypothesis template for this benchmark is `Kalimat ini mengekspresikan perasaan {}.` and `{}.`. Take note F1 score measurement only calculate label with highest probability. | Model | Multi-label | Use template | F1 Score | | -------------------------------------------- | ----------- | ------------ | ------------ | | bigbird-small-indonesian-nli | V | V | 0.3574 | | | V | | 0.3654 | | | | V | 0.3985 | | | | | _0.4160_ | | xlm-roberta-large-xnli | V | V | _**0.6292**_ | | | V | | 0.5596 | | | | V | 0.5737 | | | | | 0.5433 | | mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 | V | V | 0.5324 | | | V | | _0.5499_ | | | | V | 0.5269 | | | | | 0.5228 | | 8420bcc3b33b4625039c3adc144cb08f |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-wikihow_3epoch_b4_lr3e-5 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset. It achieves the following results on the evaluation set: - Loss: 2.4351 - Rouge1: 26.1071 - Rouge2: 9.3627 - Rougel: 22.0825 - Rougelsum: 25.4514 - Gen Len: 18.474 | d1fab528ce41493bdbad53a62a684586 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-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: 3 - mixed_precision_training: Native AMP | b248a15cf2df5359583fd033054a941f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.9216 | 0.13 | 5000 | 2.6385 | 23.8039 | 7.8863 | 20.0109 | 23.0802 | 18.3481 | | 2.8158 | 0.25 | 10000 | 2.5884 | 24.2567 | 8.2003 | 20.438 | 23.5325 | 18.3833 | | 2.7743 | 0.38 | 15000 | 2.5623 | 24.8471 | 8.3768 | 20.8711 | 24.1114 | 18.2901 | | 2.7598 | 0.51 | 20000 | 2.5368 | 25.1566 | 8.6721 | 21.1896 | 24.4558 | 18.3561 | | 2.7192 | 0.64 | 25000 | 2.5220 | 25.3477 | 8.8106 | 21.3799 | 24.6742 | 18.3108 | | 2.7207 | 0.76 | 30000 | 2.5114 | 25.5912 | 8.998 | 21.5508 | 24.9344 | 18.3445 | | 2.7041 | 0.89 | 35000 | 2.4993 | 25.457 | 8.8644 | 21.4516 | 24.7965 | 18.4354 | | 2.687 | 1.02 | 40000 | 2.4879 | 25.5886 | 8.9766 | 21.6794 | 24.9512 | 18.4035 | | 2.6652 | 1.14 | 45000 | 2.4848 | 25.7367 | 9.078 | 21.7096 | 25.0924 | 18.4328 | | 2.6536 | 1.27 | 50000 | 2.4761 | 25.7368 | 9.1609 | 21.729 | 25.0866 | 18.3117 | | 2.6589 | 1.4 | 55000 | 2.4702 | 25.7738 | 9.1413 | 21.7492 | 25.114 | 18.4862 | | 2.6384 | 1.53 | 60000 | 2.4620 | 25.7433 | 9.1356 | 21.8198 | 25.0896 | 18.489 | | 2.6337 | 1.65 | 65000 | 2.4595 | 26.0919 | 9.2605 | 21.9447 | 25.4065 | 18.4083 | | 2.6375 | 1.78 | 70000 | 2.4557 | 26.0912 | 9.3469 | 22.0182 | 25.4428 | 18.4133 | | 2.6441 | 1.91 | 75000 | 2.4502 | 26.1366 | 9.3143 | 22.058 | 25.4673 | 18.4972 | | 2.6276 | 2.03 | 80000 | 2.4478 | 25.9929 | 9.2464 | 21.9271 | 25.3263 | 18.469 | | 2.6062 | 2.16 | 85000 | 2.4467 | 26.0465 | 9.3166 | 22.0342 | 25.3998 | 18.3777 | | 2.6126 | 2.29 | 90000 | 2.4407 | 26.1953 | 9.3848 | 22.1148 | 25.5161 | 18.467 | | 2.6182 | 2.42 | 95000 | 2.4397 | 26.1331 | 9.3626 | 22.1076 | 25.4627 | 18.4413 | | 2.6041 | 2.54 | 100000 | 2.4375 | 26.1301 | 9.3567 | 22.0869 | 25.465 | 18.4929 | | 2.5996 | 2.67 | 105000 | 2.4367 | 26.0956 | 9.3314 | 22.063 | 25.4242 | 18.5074 | | 2.6144 | 2.8 | 110000 | 2.4355 | 26.1764 | 9.4157 | 22.1231 | 25.5175 | 18.4729 | | 2.608 | 2.93 | 115000 | 2.4351 | 26.1071 | 9.3627 | 22.0825 | 25.4514 | 18.474 | | 9e54e0d770d22bdcd182907f070d8bde |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Irish This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | ebe4c0f5665039960476fd988a278215 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ga") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ga") ``` | eaf48fdc3c5438aa634a1b50541af92b |
mit | ['generated_from_keras_callback'] | false | nandysoham/Wayback_Machine-clustered This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3070 - Train End Logits Accuracy: 0.9410 - Train Start Logits Accuracy: 0.8924 - Validation Loss: 0.4163 - Validation End Logits Accuracy: 0.6667 - Validation Start Logits Accuracy: 1.0 - Epoch: 0 | 71ea65c438944c80a51b35afd9aefd41 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.3070 | 0.9410 | 0.8924 | 0.4163 | 0.6667 | 1.0 | 0 | | a674c1a2ea86f37f35e99eb30659f772 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-06 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 2af1720d4458348ac2730bd255c64ba5 |
apache-2.0 | ['generated_from_trainer'] | false | favs_filter_classification_v2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the filter_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2016 - F1: 0.9762 - Roc Auc: 0.9844 - Accuracy: 0.9545 | fdf961a9eb1abdfee5d3094a50f34ab1 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.5e-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: 20 | cc0496eb14578c39408d4116b8c7ef17 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | 0.6596 | 1.0 | 16 | 0.6086 | 0.2687 | 0.5474 | 0.0 | | 0.5448 | 2.0 | 32 | 0.5354 | 0.3824 | 0.6063 | 0.0 | | 0.5106 | 3.0 | 48 | 0.4874 | 0.4444 | 0.6382 | 0.0455 | | 0.4353 | 4.0 | 64 | 0.4301 | 0.5352 | 0.6889 | 0.1818 | | 0.3699 | 5.0 | 80 | 0.3890 | 0.6579 | 0.7640 | 0.3636 | | 0.349 | 6.0 | 96 | 0.3663 | 0.6667 | 0.7633 | 0.3182 | | 0.3104 | 7.0 | 112 | 0.3327 | 0.7105 | 0.7953 | 0.4545 | | 0.3023 | 8.0 | 128 | 0.2971 | 0.7733 | 0.8303 | 0.5455 | | 0.2676 | 9.0 | 144 | 0.2766 | 0.8395 | 0.8861 | 0.7727 | | 0.2374 | 10.0 | 160 | 0.2541 | 0.8537 | 0.8980 | 0.7727 | | 0.2238 | 11.0 | 176 | 0.2399 | 0.9024 | 0.9293 | 0.8182 | | 0.2084 | 12.0 | 192 | 0.2221 | 0.9286 | 0.9531 | 0.8636 | | 0.2143 | 13.0 | 208 | 0.2138 | 0.9286 | 0.9531 | 0.8636 | | 0.1846 | 14.0 | 224 | 0.2016 | 0.9762 | 0.9844 | 0.9545 | | 0.1812 | 15.0 | 240 | 0.1957 | 0.9762 | 0.9844 | 0.9545 | | 0.1756 | 16.0 | 256 | 0.1881 | 0.9647 | 0.9806 | 0.9091 | | 0.1662 | 17.0 | 272 | 0.1845 | 0.9762 | 0.9844 | 0.9545 | | 0.1715 | 18.0 | 288 | 0.1802 | 0.9762 | 0.9844 | 0.9545 | | 0.1585 | 19.0 | 304 | 0.1782 | 0.9762 | 0.9844 | 0.9545 | | 0.1595 | 20.0 | 320 | 0.1775 | 0.9762 | 0.9844 | 0.9545 | | bb842994141a8d0653d7e9551089fa5b |
apache-2.0 | ['audio', 'speech', 'speech-gender-recognition'] | false | requirement packages !pip install git+https://github.com/huggingface/datasets.git !pip install git+https://github.com/huggingface/transformers.git !pip install torchaudio !pip install librosa ``` ```bash !git clone https://github.com/m3hrdadfi/soxan.git . ``` | 585b99ae85b5a6cb9592bc428cee3198 |
apache-2.0 | ['audio', 'speech', 'speech-gender-recognition'] | false | Prediction ```python import torch import torch.nn as nn import torch.nn.functional as F import torchaudio from transformers import AutoConfig, Wav2Vec2FeatureExtractor from src.models import Wav2Vec2ForSpeechClassification, HubertForSpeechClassification import librosa import IPython.display as ipd import numpy as np import pandas as pd ``` ```python device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model_name_or_path = "m3hrdadfi/hubert-base-persian-speech-gender-recognition" config = AutoConfig.from_pretrained(model_name_or_path) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path) sampling_rate = feature_extractor.sampling_rate model = HubertForSpeechClassification.from_pretrained(model_name_or_path).to(device) ``` ```python def speech_file_to_array_fn(path, sampling_rate): speech_array, _sampling_rate = torchaudio.load(path) resampler = torchaudio.transforms.Resample(_sampling_rate) speech = resampler(speech_array).squeeze().numpy() return speech def predict(path, sampling_rate): speech = speech_file_to_array_fn(path, sampling_rate) inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True) inputs = {key: inputs[key].to(device) for key in inputs} with torch.no_grad(): logits = model(**inputs).logits scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0] outputs = [{"Label": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)] return outputs ``` ```python path = "/path/to/female.wav" outputs = predict(path, sampling_rate) ``` ```bash [{'Label': 'F', 'Score': '98.2%'}, {'Label': 'M', 'Score': '1.8%'}] ``` | 481939580c47c91526326d16030db4e3 |
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