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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cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Yksi, kaksi, kolme, neljä, viisi, kuusi, seitsemän, kahdeksan, yhdeksän, kymmenen. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-uk-fi") print(pipe("Африка є колискою людства.")) | fa28b608f56e1758e33a6251b73fa9e1 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+pft+pbt_transformer-align_2022-03-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-fin/opusTCv20210807+pft+pbt_transformer-align_2022-03-17.test.txt) * test set scores: [opusTCv20210807+pft+pbt_transformer-align_2022-03-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-fin/opusTCv20210807+pft+pbt_transformer-align_2022-03-17.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | | 3a604a57f9d5a74b5a309254ae9582a9 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.4990 - F1: 0.7093 | 358667784eea0520713eb61fc34ab528 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8727 | 1.0 | 295 | 0.5063 | 0.6186 | | 0.4633 | 2.0 | 590 | 0.5089 | 0.6561 | | 0.3075 | 3.0 | 885 | 0.4990 | 0.7093 | | e4bcd4707c5c3418a58f09dd404e937d |
apache-2.0 | ['generated_from_trainer'] | false | SST2_DistilBERT_5E 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: - Loss: 0.4125 - Accuracy: 0.8933 | ee1d8290a73784f3201fe91222d28c04 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6744 | 0.12 | 50 | 0.6094 | 0.66 | | 0.4942 | 0.23 | 100 | 0.3772 | 0.8667 | | 0.3857 | 0.35 | 150 | 0.3256 | 0.8867 | | 0.3483 | 0.46 | 200 | 0.3634 | 0.84 | | 0.3235 | 0.58 | 250 | 0.3338 | 0.8733 | | 0.3129 | 0.69 | 300 | 0.3482 | 0.8667 | | 0.3573 | 0.81 | 350 | 0.3632 | 0.8333 | | 0.3266 | 0.92 | 400 | 0.3274 | 0.86 | | 0.2615 | 1.04 | 450 | 0.3400 | 0.8667 | | 0.2409 | 1.15 | 500 | 0.3541 | 0.8467 | | 0.2508 | 1.27 | 550 | 0.2997 | 0.88 | | 0.2442 | 1.39 | 600 | 0.3654 | 0.86 | | 0.2625 | 1.5 | 650 | 0.3302 | 0.8667 | | 0.1983 | 1.62 | 700 | 0.3184 | 0.8867 | | 0.2356 | 1.73 | 750 | 0.3239 | 0.8867 | | 0.2078 | 1.85 | 800 | 0.2968 | 0.9 | | 0.2343 | 1.96 | 850 | 0.3148 | 0.8933 | | 0.1544 | 2.08 | 900 | 0.3535 | 0.9 | | 0.1407 | 2.19 | 950 | 0.3603 | 0.8733 | | 0.187 | 2.31 | 1000 | 0.3843 | 0.88 | | 0.144 | 2.42 | 1050 | 0.4546 | 0.8467 | | 0.1786 | 2.54 | 1100 | 0.3681 | 0.88 | | 0.1315 | 2.66 | 1150 | 0.3806 | 0.8867 | | 0.1399 | 2.77 | 1200 | 0.3880 | 0.8867 | | 0.1905 | 2.89 | 1250 | 0.3944 | 0.8733 | | 0.2043 | 3.0 | 1300 | 0.3974 | 0.8733 | | 0.1081 | 3.12 | 1350 | 0.3731 | 0.9067 | | 0.1055 | 3.23 | 1400 | 0.3809 | 0.8867 | | 0.1092 | 3.35 | 1450 | 0.3568 | 0.9 | | 0.0981 | 3.46 | 1500 | 0.3610 | 0.9133 | | 0.109 | 3.58 | 1550 | 0.4126 | 0.8867 | | 0.1001 | 3.7 | 1600 | 0.3831 | 0.9 | | 0.1027 | 3.81 | 1650 | 0.4064 | 0.9 | | 0.133 | 3.93 | 1700 | 0.3845 | 0.9 | | 0.1031 | 4.04 | 1750 | 0.3915 | 0.9 | | 0.0772 | 4.16 | 1800 | 0.3988 | 0.8867 | | 0.0785 | 4.27 | 1850 | 0.3962 | 0.9 | | 0.1059 | 4.39 | 1900 | 0.3969 | 0.9 | | 0.0668 | 4.5 | 1950 | 0.4095 | 0.8933 | | 0.0915 | 4.62 | 2000 | 0.4077 | 0.8933 | | 0.1413 | 4.73 | 2050 | 0.4004 | 0.9067 | | 0.0727 | 4.85 | 2100 | 0.4100 | 0.8933 | | 0.0724 | 4.97 | 2150 | 0.4125 | 0.8933 | | 0b683aaa1e8bbc14ca5349f82d13174f |
apache-2.0 | ['generated_from_keras_callback'] | false | hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep10 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.2895 - Epoch: 9 | 7cf36e9981153eb9b91058220381ef6e |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Epoch | |:----------:|:-----:| | 4.1298 | 0 | | 3.5157 | 1 | | 3.4732 | 2 | | 3.4565 | 3 | | 3.4444 | 4 | | 3.4349 | 5 | | 3.4197 | 6 | | 3.4109 | 7 | | 3.3493 | 8 | | 3.2895 | 9 | | 0de823b1b4c29295e689eb6728fb2bae |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-toxic This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2768 | 2c41f8991b0ee0e871a7946d9131287d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5338 | 1.0 | 313 | 2.3127 | | 2.4482 | 2.0 | 626 | 2.2985 | | 2.4312 | 3.0 | 939 | 2.2411 | | 23e42059d0142bb096c8fb3b2c687b42 |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_100k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 0, Step 100k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model | 9cc84c278b23795962b4dc153993cb32 |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_100k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0-step_100k') model = TFBertModel.from_pretrained("google/multiberts-seed_0-step_100k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0-step_100k') model = BertModel.from_pretrained("google/multiberts-seed_0-step_100k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 2e5114ab5ab2a3ff9db86d059455d04c |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model Details Neural machine translation model for translating from Italic languages (itc) to Hebrew (he). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Translation (transformer-big) - **Release**: 2022-08-03 - **License:** CC-BY-4.0 - **Language(s):** - Source Language(s): cat fra glg ita lad_Latn por ron spa - Target Language(s): heb - Language Pair(s): cat-heb fra-heb glg-heb ita-heb por-heb ron-heb spa-heb - Valid Target Language Labels: - **Original Model**: [opusTCv20210807_transformer-big_2022-08-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-heb/opusTCv20210807_transformer-big_2022-08-03.zip) - **Resources for more information:** - [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) - More information about released models for this language pair: [OPUS-MT itc-heb README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/itc-heb/README.md) - [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian) - [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/ | a07f03aa937f27b8d4983f281cdc2254 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | How to Get Started With the Model A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "La María és feminista.", "Contribuyan en Tatoeba." ] model_name = "pytorch-models/opus-mt-tc-big-itc-he" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) | c66f12578d828b5194ee53875202f27b |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | תרום לטאטואבה. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-itc-he") print(pipe("La María és feminista.")) | 0c763496713964e4055cedbf94e25860 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Training - **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) - **Pre-processing**: SentencePiece (spm32k,spm32k) - **Model Type:** transformer-big - **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-08-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-heb/opusTCv20210807_transformer-big_2022-08-03.zip) - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) | 302d32bcb556d06ae697dd7b503e0b54 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-08-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-heb/opusTCv20210807_transformer-big_2022-08-03.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-08-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-heb/opusTCv20210807_transformer-big_2022-08-03.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | | 0cea89b439889796022dc5dc3639208d |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | fra-heb | tatoeba-test-v2021-08-07 | 0.60539 | 39.6 | 3281 | 20655 | | ita-heb | tatoeba-test-v2021-08-07 | 0.60264 | 40.0 | 1706 | 9796 | | por-heb | tatoeba-test-v2021-08-07 | 0.63087 | 44.4 | 719 | 4423 | | spa-heb | tatoeba-test-v2021-08-07 | 0.63883 | 44.5 | 1849 | 12112 | | cat-heb | flores101-devtest | 0.52457 | 23.0 | 1012 | 20749 | | fra-heb | flores101-devtest | 0.52953 | 23.2 | 1012 | 20749 | | glg-heb | flores101-devtest | 0.50918 | 20.8 | 1012 | 20749 | | ita-heb | flores101-devtest | 0.49007 | 18.3 | 1012 | 20749 | | por-heb | flores101-devtest | 0.53906 | 24.4 | 1012 | 20749 | | ron-heb | flores101-devtest | 0.52103 | 22.1 | 1012 | 20749 | | spa-heb | flores101-devtest | 0.47646 | 16.5 | 1012 | 20749 | | b240c15279912fc49457cf74a2a2a41b |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny Indonesian This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_11_0 id dataset. It achieves the following results on the evaluation set: - Loss: 0.6202 - Wer: 32.4218 | c4c522e8ba237d1e9f323937036e42b9 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP | a65d26e4b3046d23442420e66a4fb19c |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3823 | 4.95 | 500 | 0.5251 | 33.4732 | | 0.0495 | 9.9 | 1000 | 0.5700 | 33.3902 | | 0.0077 | 14.85 | 1500 | 0.6202 | 32.4218 | | 0.0031 | 19.8 | 2000 | 0.6616 | 32.5371 | | 0.0019 | 24.75 | 2500 | 0.6873 | 32.7954 | | 0.0014 | 29.7 | 3000 | 0.7056 | 33.5700 | | 0.0011 | 34.65 | 3500 | 0.7204 | 33.7960 | | 0.0009 | 39.6 | 4000 | 0.7327 | 33.7729 | | 0.0008 | 44.55 | 4500 | 0.7400 | 33.9113 | | 0.0007 | 49.5 | 5000 | 0.7428 | 33.3441 | | 1a4d28ba05e034d00ded2b9a0d9045bd |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_80k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 3, Step 80k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model | ba9876779c712c1658e03c6998a89cd1 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_80k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_80k') model = TFBertModel.from_pretrained("google/multiberts-seed_3-step_80k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_80k') model = BertModel.from_pretrained("google/multiberts-seed_3-step_80k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 920438706c5fe8bf2b73a53d63f26955 |
apache-2.0 | ['masked-image-modeling', 'generated_from_trainer'] | false | dit-base-manuscripts This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the davanstrien/iiif_manuscripts_label_ge_50 dataset. It achieves the following results on the evaluation set: - Loss: 1.1266 | 02f803b0e32d3164ceb097f980c62421 |
apache-2.0 | ['masked-image-modeling', '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: 1333 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 | a3c37374d4247975b4cbd298ecb53620 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 49a284e69308d81c142b89795de255b4ce290c54 pip install -e . cd egs2/talromur/tts1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/GunnarThor_talromur_c_fastspeech2 ``` | eb6a45cf0b8866d7a6819949b3597dec |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | TTS config <details><summary>expand</summary> ``` config: conf/tuning/train_fastspeech2.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/c/tts_train_fastspeech2_raw_phn_none 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: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min - - train - loss - min keep_nbest_models: 5 nbest_averaging_interval: 0 grad_clip: 1.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 8 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: 800 batch_size: 20 valid_batch_size: null batch_bins: 2500000 valid_batch_bins: null train_shape_file: - exp/c/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/text_shape.phn - exp/c/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/speech_shape valid_shape_file: - exp/c/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/valid/text_shape.phn - exp/c/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/valid/speech_shape batch_type: numel valid_batch_type: null fold_length: - 150 - 204800 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_c_phn/text - text - text - - exp/c/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/train_c_phn/durations - durations - text_int - - dump/raw/train_c_phn/wav.scp - speech - sound valid_data_path_and_name_and_type: - - dump/raw/dev_c_phn/text - text - text - - exp/c/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/dev_c_phn/durations - durations - text_int - - dump/raw/dev_c_phn/wav.scp - speech - sound allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 1.0 scheduler: noamlr scheduler_conf: model_size: 384 warmup_steps: 4000 token_list: - <blank> - <unk> - ',' - . - r - t - n - a0 - s - I0 - D - l - m - Y0 - v - h - E1 - k - a:1 - E:1 - G - f - j - T - a1 - p - c - au:1 - i:1 - O:1 - I:1 - E0 - I1 - r_0 - t_h - k_h - Y1 - ei1 - i0 - ou:1 - ei:1 - u:1 - O1 - N - l_0 - '91' - ai0 - au1 - ou0 - n_0 - ei0 - ai:1 - O0 - ou1 - i1 - ai1 - '9:1' - '90' - au0 - x - c_h - 9i:1 - C - p_h - u0 - Y:1 - J - 9i1 - u1 - 9i0 - N_0 - m_0 - J_0 - Oi1 - Yi0 - Yi1 - Oi0 - au:0 - '9:0' - E:0 - <sos/eos> odim: null model_conf: {} use_preprocessor: true token_type: phn bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null feats_extract: fbank feats_extract_conf: n_fft: 1024 hop_length: 256 win_length: null fs: 22050 fmin: 80 fmax: 7600 n_mels: 80 normalize: global_mvn normalize_conf: stats_file: exp/c/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/feats_stats.npz tts: fastspeech2 tts_conf: adim: 384 aheads: 2 elayers: 4 eunits: 1536 dlayers: 4 dunits: 1536 positionwise_layer_type: conv1d positionwise_conv_kernel_size: 3 duration_predictor_layers: 2 duration_predictor_chans: 256 duration_predictor_kernel_size: 3 postnet_layers: 5 postnet_filts: 5 postnet_chans: 256 use_masking: true use_scaled_pos_enc: true encoder_normalize_before: true decoder_normalize_before: true reduction_factor: 1 init_type: xavier_uniform init_enc_alpha: 1.0 init_dec_alpha: 1.0 transformer_enc_dropout_rate: 0.2 transformer_enc_positional_dropout_rate: 0.2 transformer_enc_attn_dropout_rate: 0.2 transformer_dec_dropout_rate: 0.2 transformer_dec_positional_dropout_rate: 0.2 transformer_dec_attn_dropout_rate: 0.2 pitch_predictor_layers: 5 pitch_predictor_chans: 256 pitch_predictor_kernel_size: 5 pitch_predictor_dropout: 0.5 pitch_embed_kernel_size: 1 pitch_embed_dropout: 0.0 stop_gradient_from_pitch_predictor: true energy_predictor_layers: 2 energy_predictor_chans: 256 energy_predictor_kernel_size: 3 energy_predictor_dropout: 0.5 energy_embed_kernel_size: 1 energy_embed_dropout: 0.0 stop_gradient_from_energy_predictor: false pitch_extract: dio pitch_extract_conf: fs: 22050 n_fft: 1024 hop_length: 256 f0max: 400 f0min: 80 reduction_factor: 1 pitch_normalize: global_mvn pitch_normalize_conf: stats_file: exp/c/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/pitch_stats.npz energy_extract: energy energy_extract_conf: fs: 22050 n_fft: 1024 hop_length: 256 win_length: null reduction_factor: 1 energy_normalize: global_mvn energy_normalize_conf: stats_file: exp/c/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/energy_stats.npz required: - output_dir - token_list version: 0.10.7a1 distributed: false ``` </details> | 2ae79e1ca9611cd59d3c628c63ed3fb8 |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-uncased_stereoset_finetuned This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the stereoset dataset. It achieves the following results on the evaluation set: - Loss: 1.0729 - Accuracy: 0.7716 | b5543fb054202bf0b6bfce8246ea923e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.21 | 5 | 0.6925 | 0.5071 | | No log | 0.42 | 10 | 0.6978 | 0.5008 | | No log | 0.62 | 15 | 0.6891 | 0.5275 | | No log | 0.83 | 20 | 0.6850 | 0.5487 | | No log | 1.04 | 25 | 0.7521 | 0.5126 | | No log | 1.25 | 30 | 0.6577 | 0.6177 | | No log | 1.46 | 35 | 0.6759 | 0.5440 | | No log | 1.67 | 40 | 0.6395 | 0.6405 | | No log | 1.88 | 45 | 0.6064 | 0.6719 | | No log | 2.08 | 50 | 0.5822 | 0.6986 | | No log | 2.29 | 55 | 0.5566 | 0.7096 | | No log | 2.5 | 60 | 0.5411 | 0.7331 | | No log | 2.71 | 65 | 0.5448 | 0.7551 | | No log | 2.92 | 70 | 0.5384 | 0.7339 | | No log | 3.12 | 75 | 0.5487 | 0.7535 | | No log | 3.33 | 80 | 0.5572 | 0.7567 | | No log | 3.54 | 85 | 0.5763 | 0.7614 | | No log | 3.75 | 90 | 0.5756 | 0.7645 | | No log | 3.96 | 95 | 0.5524 | 0.7645 | | No log | 4.17 | 100 | 0.6320 | 0.7614 | | No log | 4.38 | 105 | 0.6512 | 0.7575 | | No log | 4.58 | 110 | 0.6582 | 0.7606 | | No log | 4.79 | 115 | 0.6731 | 0.7669 | | No log | 5.0 | 120 | 0.6944 | 0.7575 | | No log | 5.21 | 125 | 0.7142 | 0.7575 | | No log | 5.42 | 130 | 0.7004 | 0.7645 | | No log | 5.62 | 135 | 0.6794 | 0.7630 | | No log | 5.83 | 140 | 0.7108 | 0.7606 | | No log | 6.04 | 145 | 0.7730 | 0.7590 | | No log | 6.25 | 150 | 0.8083 | 0.7614 | | No log | 6.46 | 155 | 0.8361 | 0.7653 | | No log | 6.67 | 160 | 0.8498 | 0.7692 | | No log | 6.88 | 165 | 0.8769 | 0.7700 | | No log | 7.08 | 170 | 0.8324 | 0.7582 | | No log | 7.29 | 175 | 0.7945 | 0.7645 | | No log | 7.5 | 180 | 0.8480 | 0.7684 | | No log | 7.71 | 185 | 0.8905 | 0.7724 | | No log | 7.92 | 190 | 0.9560 | 0.7700 | | No log | 8.12 | 195 | 0.9976 | 0.7669 | | No log | 8.33 | 200 | 1.0315 | 0.7677 | | No log | 8.54 | 205 | 1.0413 | 0.7692 | | No log | 8.75 | 210 | 1.0216 | 0.7708 | | No log | 8.96 | 215 | 1.0251 | 0.7716 | | No log | 9.17 | 220 | 1.0483 | 0.7716 | | No log | 9.38 | 225 | 1.0616 | 0.7716 | | No log | 9.58 | 230 | 1.0703 | 0.7708 | | No log | 9.79 | 235 | 1.0731 | 0.7732 | | No log | 10.0 | 240 | 1.0729 | 0.7716 | | 15ec2aedb45d5082113ed5b888d6ef00 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-small_talk-2-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3566 - Accuracy: 0.3855 | 9c7b93e01f4ef1b69f7311d1c6e4f83f |
['apache-2.0'] | ['xlnet', 'lm-head', 'causal-lm'] | false | Model description This model require Mecab and senetencepiece with XLNetTokenizer. See details https://qiita.com/mkt3/items/4d0ae36f3f212aee8002 This model uses NFKD as the normalization method for character encoding. Japanese muddle marks and semi-muddle marks will be lost. *日本語の濁点・半濁点がないモデルです* | 8f8ef6a60ef8bcf49bb9d13028bd04cd |
['apache-2.0'] | ['xlnet', 'lm-head', 'causal-lm'] | false | How to use ```python from fugashi import Tagger from transformers import ( pipeline, XLNetLMHeadModel, XLNetTokenizer ) class XLNet(): def __init__(self): self.m = Tagger('-Owakati') self.gen_model = XLNetLMHeadModel.from_pretrained("hajime9652/xlnet-japanese") self.gen_tokenizer = XLNetTokenizer.from_pretrained("hajime9652/xlnet-japanese") def generate(self, prompt="福岡のご飯は美味しい。コンパクトで暮らしやすい街。"): prompt = self.m.parse(prompt) inputs = self.gen_tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") prompt_length = len(self.gen_tokenizer.decode(inputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)) outputs = self.gen_model.generate(inputs, max_length=200, do_sample=True, top_p=0.95, top_k=60) generated = prompt + self.gen_tokenizer.decode(outputs[0])[prompt_length:] return generated ``` | 019dc81e00a65ec7a64cdb1af293dd85 |
['apache-2.0'] | ['xlnet', 'lm-head', 'causal-lm'] | false | Important matter The company that created and published this model is called Stockmark. This repository is for use by HuggingFace and not for infringement. See this documents https://qiita.com/mkt3/items/4d0ae36f3f212aee8002 published by https://github.com/mkt3 | 1f61bd259c4737ae7e76d2e43e3234b2 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0639 - Precision: 0.9357 - Recall: 0.9507 - F1: 0.9432 - Accuracy: 0.9857 | 903243283c940dcaa5ea36d72899ac5c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0847 | 1.0 | 1756 | 0.0636 | 0.9150 | 0.9387 | 0.9267 | 0.9840 | | 0.0399 | 2.0 | 3512 | 0.0592 | 0.9302 | 0.9485 | 0.9393 | 0.9854 | | 0.0201 | 3.0 | 5268 | 0.0639 | 0.9357 | 0.9507 | 0.9432 | 0.9857 | | c983e96404a21580ea85eba61aece82e |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_hubert_s451 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 6307da40ad7674228a9e909a27482781 |
apache-2.0 | ['translation'] | false | kor-spa * source group: Korean * target group: Spanish * OPUS readme: [kor-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/kor-spa/README.md) * model: transformer-align * source language(s): kor kor_Hang kor_Latn * target language(s): spa * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/kor-spa/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/kor-spa/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/kor-spa/opus-2020-06-17.eval.txt) | 85102e1e40d822bcf64b9b966e9d6391 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: kor-spa - source_languages: kor - target_languages: spa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/kor-spa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ko', 'es'] - src_constituents: {'kor_Hani', 'kor_Hang', 'kor_Latn', 'kor'} - tgt_constituents: {'spa'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/kor-spa/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/kor-spa/opus-2020-06-17.test.txt - src_alpha3: kor - tgt_alpha3: spa - short_pair: ko-es - chrF2_score: 0.521 - bleu: 31.3 - brevity_penalty: 0.95 - ref_len: 6805.0 - src_name: Korean - tgt_name: Spanish - train_date: 2020-06-17 - src_alpha2: ko - tgt_alpha2: es - prefer_old: False - long_pair: kor-spa - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 5c09ca791a3b47bdc3ac56d210785378 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_data_aug_qnli_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4463 - Accuracy: 0.5576 | ceaf244405ecfca07485f56308312f8f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.338 | 1.0 | 16604 | 0.4463 | 0.5576 | | 0.2791 | 2.0 | 33208 | 0.4560 | 0.5711 | | 0.256 | 3.0 | 49812 | 0.4603 | 0.5691 | | 0.2446 | 4.0 | 66416 | 0.4620 | 0.5709 | | 0.2379 | 5.0 | 83020 | 0.4547 | 0.5958 | | 0.2334 | 6.0 | 99624 | 0.4581 | 0.5863 | | 3cbcd711ae716ff39736c51283395802 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/vctk_tts_train_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4394600/ This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 48330a61c967c5ed5e58790e0c5f21f1 |
creativeml-openrail-m | ['text-to-image'] | false | Duskfall's Digital Fantasy Dreambooth model trained by Duskfallcrew 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! All samples and info are here: https://civitai.com/user/duskfallcrew If you want to donate towards costs and don't want to subscribe: https://ko-fi.com/DUSKFALLcrew If you want to monthly support the EARTH & DUSK media projects and not just AI: https://www.patreon.com/earthndusk digidsk1 (use that on your prompt) | bdf67bb43805d121d8614d550ef1cf69 |
apache-2.0 | ['generated_from_trainer'] | false | M4_MLM 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: - Loss: 7.3456 | 6d0dd372fcd3cfd79ad1d7541e7442ee |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 8.7633 | 1.0 | 26 | 8.0400 | | 7.8899 | 2.0 | 52 | 7.6923 | | 7.589 | 3.0 | 78 | 7.4373 | | 0985edbac5ce8875df411e6f1d397363 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xlsr-53-espeak-cv-ft-sah-ntsema-colab This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2143 - Wer: 0.2247 | 5ace61071cc0992a6ecae56f0ed930a3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.7431 | 5.71 | 400 | 0.2879 | 0.4054 | | 0.1876 | 11.42 | 800 | 0.2349 | 0.3023 | | 0.0986 | 17.14 | 1200 | 0.2248 | 0.2701 | | 0.0737 | 22.85 | 1600 | 0.2242 | 0.2428 | | 0.0546 | 28.57 | 2000 | 0.2143 | 0.2247 | | 88239ad22822c94ca6b0d3e45601da77 |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_vp-100k_gender_male-10_female-0_s504 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 28d970fe40828d83a086272d23fc766d |
apache-2.0 | ['generated_from_trainer', 'summarization'] | false | arxiv27k-t5-abst-title-gen/ This model is a fine-tuned version of mt5-small on the arxiv-abstract-title dataset. It achieves the following results on the evaluation set: - Loss: 1.6002 - Rouge1: 32.8 - Rouge2: 21.9 - Rougel: 34.8 - | 8dcc6bae56791bce5c339308ceb7bca9 |
apache-2.0 | ['generated_from_trainer', 'summarization'] | false | Training args model_args = T5Args() model_args.max_seq_length = 256 model_args.train_batch_size = 8 model_args.eval_batch_size = 8 model_args.num_train_epochs = 6 model_args.evaluate_during_training = False model_args.use_multiprocessing = False model_args.fp16 = False model_args.save_steps = 40000 model_args.save_eval_checkpoints = False model_args.save_model_every_epoch = True model_args.output_dir = OUTPUT_DIR model_args.no_cache = True model_args.reprocess_input_data = True model_args.overwrite_output_dir = True model_args.num_return_sequences = 1 | 66a15d88271e0d5c70e7243c53baa298 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Gerph Welcome to the Gerph model. This model is trained in the art of the talented artist Gerph and has three versions for you to choose from. These models can be highly NSFW and are trained mainly on characters, as the work primarily focuses on this subject. Take a look at the demo images below to see the differences between the three versions. And don't forget that these models is licensed under the Creative ML OpenRAIL-M license. Enjoy! **Gerph_Epoch8**  > highres, best quality, masterpiece, hatsune miku, outside, sunny day, casual clothes **Gerph_Epoch10**  > close up, male, solo, long hair, blonde hair, blue eyes, bishounen, colorful, boy, autumn, cinematic lighting, blue sky **Gerph_Epoch11**  > young girl, brown hair, green eyes, colorful, winter, cumulonimbus clouds, lighting, blue sky As you can see, the base version, *Gerph_Epoch8*, is trained exclusively in Gerph's art and offers a unique take on his style and themes. If you are a fan of Gerph's art, this version should certainly be in your set. *Gerph_Epoch10* and *Gerph_Epoch11* were continued with a wider range of concept images and work by various artists, so unfortunately the original Gerph style doesn't shine as much. Also, these models do not require any specific tokens. | a414c859727f3e821626b706da7ecd7c |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | License These models are open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the models to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the models commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) | 3b779891747e99735a128a95fe1b7904 |
creativeml-openrail-m | [] | false | Prompt with **"hutari"** **Training details:** - Trained with [TheLastBen's fast-DreamBooth notebook](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) - data set: around 20 concept images + around 50 custmized reg images, the concept images are then duplicated to balance the two - learning rate 2e-6 for 5000 steps - text encoder rate 15% **Example generations:**      | 0cae69c29cacbcfa4c30a3353f067ec6 |
apache-2.0 | ['generated_from_trainer'] | false | canine-c-finetuned-mrpc This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4066 - Accuracy: 0.8627 - F1: 0.9014 | 453c4a66faa61ed4bad4fb2a91965e7c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 230 | 0.5014 | 0.7696 | 0.8479 | | No log | 2.0 | 460 | 0.4755 | 0.7892 | 0.8622 | | 0.5096 | 3.0 | 690 | 0.3645 | 0.8431 | 0.8869 | | 0.5096 | 4.0 | 920 | 0.4066 | 0.8627 | 0.9014 | | 0.2619 | 5.0 | 1150 | 0.4551 | 0.8431 | 0.8877 | | f7091aa0ca8be196b69af08abf58571c |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_age_teens-5_sixties-5_s62 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | e4d18fb588825a295e89f50bcc07a565 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-25000-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: 0.3711 - Accuracy: 0.9314 - F1: 0.9320 | 96174958c4542bb5e4c3db1c329c0f0b |
afl-3.0 | ['generated_from_trainer', 'sentiment', 'emotion'] | false | electricidad-small-discriminator-finetuned-clasificacion-texto-suicida
This model is a fine-tuned version of [mrm8488/electricidad-small-discriminator](https://huggingface.co/mrm8488/electricidad-small-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0458
- Accuracy: 0.9916
| 5bc4eb79add91d598f4e7fd98b3e8699 |
afl-3.0 | ['generated_from_trainer', 'sentiment', 'emotion'] | false | Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- lr_scheduler_type: linear
- num_epochs: 15
| a5430c4c545b3e367ea970cafe59ebe4 |
afl-3.0 | ['generated_from_trainer', 'sentiment', 'emotion'] | false | Training results
| Training Loss | Epoch | Validation Loss | Accuracy |
|:-------------:|:-----:|:---------------:|:--------:|
| 0.161100 | 1.0 | 0.133057 | 0.952718 |
| 0.134500 | 2.0 | 0.110966 | 0.960804 |
| 0.108500 | 3.0 | 0.086417 | 0.970835 |
| 0.099400 | 4.0 | 0.073618 | 0.974856 |
| 0.090500 | 5.0 | 0.065231 | 0.979629 |
| 0.080700 | 6.0 | 0.060849 | 0.982324 |
| 0.069200 | 7.0 | 0.054718 | 0.986125 |
| 0.060400 | 8.0 | 0.051153 | 0.985948 |
| 0.048200 | 9.0 | 0.045747 | 0.989748 |
| 0.045500 | 10.0 | 0.049992 | 0.988069 |
| 0.043400 | 11.0 | 0.046325 | 0.990234 |
| 0.034300 | 12.0 | 0.050746 | 0.989792 |
| 0.032900 | 13.0 | 0.043434 | 0.991737 |
| 0.028400 | 14.0 | 0.045003 | 0.991869 |
| 0.022300 | 15.0 | 0.045819 | 0.991648 |
| c2e06a9b7993e1da3fe699aa3e39d9f9 |
creativeml-openrail-m | [] | false | mT5-small based Azerbaijani Summarization In this model, [Google's Multilingual T5-small](https://github.com/google-research/multilingual-t5) is fine-tuned on [Azerbaijani News Summary Dataset](https://huggingface.co/datasets/nijatzeynalov/azerbaijani-multi-news) for **Summarization** downstream task. The model is trained with 3 epochs, 64 batch size and 10e-4 learning rate. It took almost 12 hours on GPU instance with Ubuntu Server 20.04 LTS image in Microsoft Azure. The max news length is kept as 2048 and max summary length is determined as 128. mT5 is a multilingual variant of __T5__ and only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4 | 0b39b4696bbc9f86a793698070b8ba98 |
creativeml-openrail-m | [] | false | Text-to-Text Transfer Transformer (T5) The paper [“Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer”](https://arxiv.org/pdf/1910.10683.pdf) presents a large-scale empirical survey to determine which transfer learning techniques work best and apply these insights at scale to create a new model called the Text-To-Text Transfer Transformer.  T5, or Text-to-Text Transfer Transformer, is a Transformer based architecture that uses a text-to-text approach. Every task – including translation, question answering, and classification – is cast as feeding the model text as input and training it to generate some target text. This allows for the use of the same model, loss function, hyperparameters, etc. across our diverse set of tasks. The changes compared to BERT include: - adding a causal decoder to the bidirectional architecture. - replacing the fill-in-the-blank cloze task with a mix of alternative pre-training tasks. The model was trained on a cleaned version of Common Crawl that is two orders of magnitude larger than Wikipedia. The T5 model, pre-trained on C4, achieves state-of-the-art results on many NLP benchmarks while being flexible enough to be fine-tuned to several downstream tasks. The pre-trained T5 in Hugging Face is also trained on the mixture of unsupervised training (which is trained by reconstructing the masked sentence) and task-specific training. | 11ea434a27fd37bfcde2b7e0b1c2eeef |
creativeml-openrail-m | [] | false | Multilingual t5 ["mt5"](https://arxiv.org/pdf/2010.11934v3.pdf) is a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. mT5 is pre-trained only by unsupervised manner with multiple languages, and it’s not trained for specific downstream tasks. To dare say, this pre-trained model has ability to build correct text in Azerbaijani, but it doesn’t have any ability for specific tasks, such as, summarization, correction, machine translation, etc. In HuggingFace, several sizes of mT5 models are available, and here I used small one (google/mt5-small). Therefore I trained (fine-tune) this model for summarization in Azerbaijani using [Azerbaijani News Summary Dataset](https://huggingface.co/datasets/nijatzeynalov/azerbaijani-multi-news). | bc9981fde6cdcf03e316d4b6628ccd5e |
creativeml-openrail-m | [] | false | Training hyperparameters __mT5-based-azerbaijani-summarize__ model training took almost 12 hours on GPU instance with Ubuntu Server 20.04 LTS image in Microsoft Azure. The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - 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: 90 - num_epochs: 10 | 44775a5ea0f23b7c27a24c11479bc94b |
creativeml-openrail-m | [] | false | Dataset Model was trained on [__az-news-summary__ dataset](https://huggingface.co/datasets/nijatzeynalov/azerbaijani-multi-news), a comprehensive and diverse dataset comprising 143k (143,448) Azerbaijani news articles extracted using a set of carefully designed heuristics. The dataset covers common topics for news reports include war, government, politics, education, health, the environment, economy, business, fashion, entertainment, and sport, as well as quirky or unusual events. This dataset has 3 splits: _train_, _validation_, and _test_. \ Token counts are white space based. | Dataset Split | Number of Instances | Size (MB) | | ------------- | --------------------|:----------------------| | Train | 100,413 | 150 | | Validation | 14,344 | 21.3 | | Test | 28,691 | 42.8 | | a3c52744f17f552c0ade056c2145c3fb |
creativeml-openrail-m | [] | false | Training results with comparison __mT5-based-azerbaijani-summarize__ model rouge scores on the test set: - Rouge1: 39.4222 - Rouge2: 24.8624 - Rougel: 32.2487 For __Azerbaijani text summarization downstream task__, mT5-multilingual-XLSum has also been developed on the 45 languages of [XL-Sum](https://huggingface.co/datasets/csebuetnlp/xlsum) dataset. For finetuning details and scripts, see the [paper](https://aclanthology.org/2021.findings-acl.413/) and the [official repository](https://github.com/csebuetnlp/xl-sum). . __mT5_multilingual_XLSum__ modelrouge scores on the XL-Sum test set (only for Azerbaijani): - Rouge1: 21.4227 - Rouge2: 9.5214 - Rougel: 19.3331 As seen from the numbers, our model __mT5-based-azerbaijani-summarize__ achieves dramatically better performance than __mT5_multilingual_XLSum__. | b5dc3208eb32b26424c21e2b47800ae7 |
creativeml-openrail-m | [] | false | Using this model in transformers ```python !pip install sentencepiece !pip install transformers ``` ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM article_text = """Ötən il Azərbaycana 74 577 avtomobil idxal edilib. Bu da 2021-ci illə müqayisədə 16 617 ədəd və ya 18,2% azdır. Xezerxeber.az-ın məlumatına görə, avtomobil bazarı üzrə qiymətləndirici Sərxan Qədirov deyib ki, əvvəl ay ərzində 5-10 avtomobil gətirən şəxslər hazırda bu sayı 2-3 ədədə endiriblər. Hətta ölkəyə nəqliyyat vasitələrinin gətirilməsi işini dayandıranlar da var. Nəqliyyat məsələləri üzrə ekspert Eldəniz Cəfərov isə bildirib ki, gözləniləndən fərqli olaraq, ölkəyə idxal olunan kiçik mühərrikli avtomobillərin sayında da azalma var. Bunun başlıca səbəbi Rusiyada istehsalın dayandırılmasıdır. Ekspertin sözlərinə görə, əvvəllər Azərbaycan bazarında Rusiya istehsalı olan nəqliyyat vasitələri geniş yer tuturdu. Hazırda isə həmin ölkədən idxal tam dayanıb.""" model_name = "nijatzeynalov/mT5-based-azerbaijani-summarize" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) ``` ```python input_ids = tokenizer( article_text, return_tensors="pt", padding="max_length", truncation=True, max_length=2048 )["input_ids"] output_ids = model.generate( input_ids=input_ids, max_length=128, no_repeat_ngram_size=2, num_beams=4 )[0] summary = tokenizer.decode( output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(summary) ``` Result: ```python Azərbaycana idxal olunan avtomobillərin sayı açıqlanıb ``` | 62afc36d4cc2ce1677ec512ae2044f2e |
creativeml-openrail-m | [] | false | Citation If you use this model, please cite: ``` @misc {nijatzeynalov_2023, author = { {NijatZeynalov} }, title = { mT5-based-azerbaijani-summarize (Revision 19930ab) }, year = 2023, url = { https://huggingface.co/nijatzeynalov/mT5-based-azerbaijani-summarize }, doi = { 10.57967/hf/0316 }, publisher = { Hugging Face } } ``` | 81775bffdc49c08b2909b0a2886494ab |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0612 - Precision: 0.9237 - Recall: 0.9343 - F1: 0.9290 - Accuracy: 0.9833 | fc725b9d412060f916db92070d375f61 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2462 | 1.0 | 878 | 0.0708 | 0.9118 | 0.9149 | 0.9133 | 0.9803 | | 0.0548 | 2.0 | 1756 | 0.0612 | 0.9218 | 0.9325 | 0.9271 | 0.9827 | | 0.0307 | 3.0 | 2634 | 0.0612 | 0.9237 | 0.9343 | 0.9290 | 0.9833 | | 33d31dce70636ab99f4abd9e755d6f11 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-EL6 (Deep-Narrow version) T5-Efficient-BASE-EL6 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. | 0d5adf608012f3d9eb3c999c80977399 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-el6** - is of model type **Base** with the following variations: - **el** is **6** It has **180.45** million parameters and thus requires *ca.* **721.8 MB** of memory in full precision (*fp32*) or **360.9 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | | 1eda830494f407f11f2eadccc670aa35 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_qqp This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.6308 - Accuracy: 0.6473 - F1: 0.0880 - Combined Score: 0.3676 | 823a4065edf5a5660e5306ea048b7980 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.7821 | 1.0 | 1422 | 0.7485 | 0.6318 | 0.0 | 0.3159 | | 0.7105 | 2.0 | 2844 | 0.7038 | 0.6364 | 0.0261 | 0.3312 | | 0.6654 | 3.0 | 4266 | 0.6862 | 0.6351 | 0.0188 | 0.3269 | | 0.6284 | 4.0 | 5688 | 0.6610 | 0.6453 | 0.0779 | 0.3616 | | 0.5969 | 5.0 | 7110 | 0.6479 | 0.6416 | 0.0554 | 0.3485 | | 0.5712 | 6.0 | 8532 | 0.6457 | 0.6404 | 0.0497 | 0.3450 | | 0.5513 | 7.0 | 9954 | 0.6308 | 0.6473 | 0.0880 | 0.3676 | | 0.5349 | 8.0 | 11376 | 0.6351 | 0.6503 | 0.1037 | 0.3770 | | 0.5222 | 9.0 | 12798 | 0.6383 | 0.6719 | 0.2134 | 0.4427 | | 0.5124 | 10.0 | 14220 | 0.6392 | 0.6685 | 0.1991 | 0.4338 | | 0.5044 | 11.0 | 15642 | 0.6379 | 0.6615 | 0.1631 | 0.4123 | | 0.4978 | 12.0 | 17064 | 0.6363 | 0.6637 | 0.1750 | 0.4194 | | 9377be9c82af5f9a349dd89d8ad3a5cb |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.4085 - F1: 0.6985 | 2ec85ef59cce4f204aaf527e9a445bed |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1067 | 1.0 | 50 | 0.6303 | 0.4922 | | 0.5183 | 2.0 | 100 | 0.4321 | 0.6524 | | 0.3688 | 3.0 | 150 | 0.4085 | 0.6985 | | 48235f3284c6a971f5795ed7895981c1 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-custom-tokenizer-expand-vocab-target-glue-cola This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola-custom-tokenizer-expand-vocab](https://huggingface.co/muhtasham/tiny-mlm-glue-cola-custom-tokenizer-expand-vocab) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7478 - Matthews Correlation: 0.0630 | 346dd9275737660da78989d04ec8bb8b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6117 | 1.87 | 500 | 0.6224 | 0.0 | | 0.5987 | 3.73 | 1000 | 0.6217 | 0.0181 | | 0.5786 | 5.6 | 1500 | 0.6271 | 0.0364 | | 0.5513 | 7.46 | 2000 | 0.6517 | 0.0412 | | 0.5219 | 9.33 | 2500 | 0.6753 | 0.1073 | | 0.5067 | 11.19 | 3000 | 0.6918 | 0.0978 | | 0.4827 | 13.06 | 3500 | 0.7235 | 0.0896 | | 0.4638 | 14.93 | 4000 | 0.7478 | 0.0630 | | 37f87d6a821ea460376812d14bb9e661 |
mit | [] | false | This model has been pretrained on MS MARCO corpus and then finetuned on MS MARCO training data with implicit distributionally robust optimization (iDRO), following the approach described in the paper **COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning**. The associated GitHub repository is available here https://github.com/OpenMatch/COCO-DR. This model is trained with BERT-large as the backbone with 335M hyperparameters. | b1c69143865555bf54c5c4c0db76e741 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Italian 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 it dataset. It achieves the following results on the evaluation set: - Loss: 0.2534 - Wer: 12.3040 | a6e892facce90d7f0b999ccb2f834767 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2737 | 2.01 | 1000 | 0.2728 | 13.4097 | | 0.1536 | 4.02 | 2000 | 0.2611 | 12.9897 | | 0.0905 | 6.03 | 3000 | 0.2686 | 12.9273 | | 0.1301 | 8.04 | 4000 | 0.2534 | 12.3040 | | 0.096 | 10.05 | 5000 | 0.2727 | 12.6130 | | 0.0604 | 12.06 | 6000 | 0.2698 | 12.5027 | | d33bbf6e95bb17598128ffe3f690b3b1 |
mit | ['generated_from_trainer'] | false | BiBert-Classification-V2 This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7627 - Accuracy: 0.8180 | 4efd0f0a1969cea80138f196b719af23 |
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: 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 - num_epochs: 3 | 040036f06c8635bec1517e2061889c9c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.8285 | 1.0 | 4290 | 0.8182 | 0.7934 | | 0.7496 | 2.0 | 8580 | 0.7750 | 0.8108 | | 0.6738 | 3.0 | 12870 | 0.7627 | 0.8180 | | c3f82c0bf302f907558abaa0b8975088 |
apache-2.0 | ['generated_from_keras_callback'] | false | distilbert1000e 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: | 2252d17596507d9a65b4f0c5fa352c8c |
mit | ['ja', 'japanese', 'gpt-neox', 'text-generation', 'lm', 'nlp'] | false | japanese-gpt-neox-small  This repository provides a small-sized Japanese GPT-NeoX model. The model was trained using code based on [EleutherAI/gpt-neox](https://github.com/EleutherAI/gpt-neox). | d0c7c213cbc76fbf40e939af652190ce |
mit | ['ja', 'japanese', 'gpt-neox', 'text-generation', 'lm', 'nlp'] | false | How to use the model *NOTE:* * Use `T5Tokenizer` to load its corresponding tokenizer. * The files for modeling and configuration are not in the Transformers library yet. In order to load the model, use files from [this PR in EleutherAI/gpt-neox](https://github.com/EleutherAI/gpt-neox/pull/480). ~~~~ from transformers import T5Tokenizer from modeling_gpt_neox import GPTNeoXForCausalLM tokenizer = T5Tokenizer.from_pretrained("rinna/japanese-gpt-neox-small") model = GPTNeoXForCausalLM.from_pretrained("rinna/japanese-gpt-neox-small") ~~~~ | 3a275e86694a2b421d51022eb96ec59c |
mit | ['ja', 'japanese', 'gpt-neox', 'text-generation', 'lm', 'nlp'] | false | Training The model was trained on [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz), [Japanese C4](https://huggingface.co/datasets/mc4), and [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) to optimize a traditional language modelling objective. | 52d40dc64355ee1eae0e82d3808c4a8e |
mit | ['ja', 'japanese', 'gpt-neox', 'text-generation', 'lm', 'nlp'] | false | A toy prefix-tuning weight file Along with pretrained model, we also release a [prefix-tuning](https://arxiv.org/abs/2101.00190) weight file named `smileface_suffix.task0.weight` for demonstration. The toy prefix-tuning weights here is trained to encourage the model to end every generated sentence with a smiling face emoji 😃. Find the training/inference code for prefix-tuning at our Github repo [prefix-tuning-gpt](https://github.com/rinnakk/prefix-tuning-gpt). Here are a few samples generated with and without the toy prefix weights, respectively. 3 samples without the prefix weights > 1. 「きっとそれは絶対間違ってないね。 わたしには5か国語に4つの外国語の意味なんてわからない。 でも、とりあえずこの簡単な英文がどんな意味を持つのか知りたいよね!」 > 2. 25分頃に公園に着いて、ベンチに座って待っていると、またしてもS先生から連絡が入りました。 確か、午後の礼拝の時に自分の持ってきたお弁当を食べた記憶が鮮明に残っています。 後でインターネットで検索したら、S先生のブログに飛びました。 今日の晩ごはんは焼きナスを作ってみました! * 上の写真は昨日の朝焼けです。 > 3. CTで歯形ができて、その後さらにその歯形が再び噛めるようになるのは、何が原因だろう? 虫歯になった原因も、口臭かな? それとも歯周病かな? 歯石がとれるまで、、、もうちょっとかかりそう。 子供の虫歯って、なかなか治らないですよね。親兄弟で何度か。 子供の歯根は、親のものになります。 そして自分のものだったり、知らない間に抜いたりし、生えてきたりもします。 大人になって親からみた場合は、白い歯に変わってきて、金属のようーでも悪くなく、親からのむし歯の心配はないですよね。 3 samples with the prefix weights: > 1. ※海外ブランド品の場合は、返品・返金等はお受け致しかねますので予めご了承願います。 ※ 商品発送後、お客様へ商品返送完了までのスピードを重視する方は海外ブランド品を先に送り付けさせて頂く ケースがございます。 😃 > 2. 私は過去に持っていた不動産を、中古住宅として売却していましたが、その後の私の状況はどうだったのでしょうか? 😃 結果としては、投資物件として売却を考えていますが、今までの相場も読んでいただけばわかると思います。 😃 今まで、物件に対しての投資は非常に控えめにしてきたのですが、今回の提案を読んで、実際に物件を購入する際にはきちんと確認をしようと思います。 😃 > 3. この写真集の表紙をこの台紙にしている作家さんは、まるで誰かの指示を受けて行動している人物のように見える、というのが、この作品をやぶにらんだ「殺し屋集団」の描いている作品であるように思 います。 😃 | d99c6cb7a1027c8329b00ccaaa5b4b25 |
mit | ['ja', 'japanese', 'gpt-neox', 'text-generation', 'lm', 'nlp'] | false | Inference with FasterTransformer After version 5.1, [NVIDIA FasterTransformer](https://github.com/NVIDIA/FasterTransformer) now supports both inference for GPT-NeoX and a variety of soft prompts (including prefix-tuning). The released pretrained model and prefix weights in this repo have been verified to work with FasterTransformer 5.1. | 54ae838aacbfe027bf38e5162424a75c |
mit | ['vision', 'video-classification'] | false | X-CLIP (base-sized model) X-CLIP model (base-sized, patch resolution of 32) trained fully-supervised on [Kinetics-400](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et al. and first released in [this repository](https://github.com/microsoft/VideoX/tree/master/X-CLIP). This model was trained using 8 frames per video, at a resolution of 224x224. Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team. | a3b93e5fba02a1c23253482596f63e5a |
apache-2.0 | ['generated_from_keras_callback'] | false | juliietth/mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.9197 - Validation Loss: 3.6988 - Epoch: 1 | a893dbae6e25a1e78ac2259fcfeebaa4 |
apache-2.0 | ['summarization'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:---------:|:-------:| | 1.2875 | 1.0 | 5754 | 1.6294 | 11.009 | 7.4618 | 10.5573 | 10.8087 | 58.3382 | | 8b7d5f2bb9b40d32de8c907e8e40e3b6 |
mit | [] | false | Model description The Time Series Transformer is a vanilla encoder-decoder Transformer for time-series forecasting. The model is trained in the same way as one trains a Transformer for machine translation. At inference time, the model autoregressively generates samples, one time step at a time. | 5faa8d129de755d0c16738e0e37ed9ae |
apache-2.0 | [] | false | Tokenizer The *WordPiece* tokenizer uses several components: * **Normalization**: lowercase and then NFKD unicode normalization. * **Pretokenization**: splits by whitespace and punctuation. * **Postprocessing**: single sentences are output in format `[CLS] sentence A [SEP]` and pair sentences in format `[CLS] sentence A [SEP] sentence B [SEP]`. | 97751473c3e5e4dbb68ee172fa15337b |
apache-2.0 | [] | false | Training Training was performed over 16M+ Dhivehi sentences/paragraphs put together by [@ashraq](https://huggingface.co/ashraq). An Adam optimizer with weighted decay was used with following parameters: * Learning rate: 1e-5 * Weight decay: 0.1 * Warmup steps: 10% of data | 8a73aef940cbf5d003c07ceec3c4472c |
apache-2.0 | ['token-classification'] | false | How to use ```python from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline model_name = "IlyaGusev/ru-word-stress-transformer" tokenizer = AutoTokenizer.from_pretrained( model_name, trust_remote_code=True, revision="bae83dd" ) model = AutoModelForTokenClassification.from_pretrained(model_name) pipe = pipeline( "token-classification", model=model, tokenizer=tokenizer, device=-1, aggregation_strategy="none", ignore_labels=("NO",) ) text = "щеколда" print(text) index = pipe(text)[0]["index"] print(text[:index] + "'" + text[index:]) ``` Colab: [link](https://colab.research.google.com/drive/1I61aDezhxMVZzHQQfpn7Wqn-ydbndO6i) | 096f93b5542dda8a34f9b7a2a1cd65f7 |
apache-2.0 | [] | false | KeyBART KeyBART as described in "Learning Rich Representations of Keyphrase from Text" published in the Findings of NAACL 2022 (https://aclanthology.org/2022.findings-naacl.67.pdf), pre-trains a BART-based architecture to produce a concatenated sequence of keyphrases in the CatSeqD format. We provide some examples on Downstream Evaluations setups and and also how it can be used for Text-to-Text Generation in a zero-shot setting. | 0961f8f733bf33e171891c968f886679 |
apache-2.0 | [] | false | Keyphrase Generation ``` from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("bloomberg/KeyBART") model = AutoModelForSeq2SeqLM.from_pretrained("bloomberg/KeyBART") from datasets import load_dataset dataset = load_dataset("midas/kp20k") ``` Reported Results: | f5d0738323a377115c44b058f6fca682 |
apache-2.0 | [] | false | Present Keyphrase Generation | | Inspec | | NUS | | Krapivin | | SemEval | | KP20k | | |---------------|--------|-------|-------|-------|----------|-------|---------|-------|-------|-------| | Model | F1@5 | F1@M | F1@5 | F1@M | F1@5 | F1@M | F1@5 | F1@M | F1@5 | F1@M | | catSeq | 22.5 | 26.2 | 32.3 | 39.7 | 26.9 | 35.4 | 24.2 | 28.3 | 29.1 | 36.7 | | catSeqTG | 22.9 | 27 | 32.5 | 39.3 | 28.2 | 36.6 | 24.6 | 29.0 | 29.2 | 36.6 | | catSeqTG-2RF1 | 25.3 | 30.1 | 37.5 | 43.3 | 30 | 36.9 | 28.7 | 32.9 | 32.1 | 38.6 | | GANMR | 25.8 | 29.9 | 34.8 | 41.7 | 28.8 | 36.9 | N/A | N/A | 30.3 | 37.8 | | ExHiRD-h | 25.3 | 29.1 | N/A | N/A | 28.6 | 34.7 | 28.4 | 33.5 | 31.1 | 37.4 | | Transformer (Ye et al., 2021) | 28.15 | 32.56 | 37.07 | 41.91 | 31.58 | 36.55 | 28.71 | 32.52 | 33.21 | 37.71 | | BART* | 23.59 | 28.46 | 35.00 | 42.65 | 26.91 | 35.37 | 26.72 | 31.91 | 29.25 | 37.51 | | KeyBART-DOC* | 24.42 | 29.57 | 31.37 | 39.24 | 24.21 | 32.60 | 24.69 | 30.50 | 28.82 | 37.59 | | KeyBART* | 24.49 | 29.69 | 34.77 | 43.57 | 29.24 | 38.62 | 27.47 | 33.54 | 30.71 | 39.76 | | KeyBART* (Zero-shot) | 30.72 | 36.89 | 18.86 | 21.67 | 18.35 | 20.46 | 20.25 | 25.82 | 12.57 | 15.41 | | 8091b3e19ab2098d3382761c16fbd916 |
apache-2.0 | [] | false | Absent Keyphrase Generation | | Inspec | | NUS | | Krapivin | | SemEval | | KP20k | | |---------------|--------|------|------|------|----------|------|---------|------|-------|------| | Model | F1@5 | F1@M | F1@5 | F1@M | F1@5 | F1@M | F1@5 | F1@M | F1@5 | F1@M | | catSeq | 0.4 | 0.8 | 1.6 | 2.8 | 1.8 | 3.6 | 1.6 | 2.8 | 1.5 | 3.2 | | catSeqTG | 0.5 | 1.1 | 1.1 | 1.8 | 1.8 | 3.4 | 1.1 | 1.8 | 1.5 | 3.2 | | catSeqTG-2RF1 | 1.2 | 2.1 | 1.9 | 3.1 | 3.0 | 5.3 | 2.1 | 3.0 | 2.7 | 5.0 | | GANMR | 1.3 | 1.9 | 2.6 | 3.8 | 4.2 | 5.7 | N/A | N/A | 3.2 | 4.5 | | ExHiRD-h | 1.1 | 2.2 | N/A | N/A | 2.2 | 4.3 | 1.7 | 2.5 | 1.6 | 3.2 | | Transformer (Ye et al., 2021) | 1.02 | 1.94 | 2.82 | 4.82 | 3.21 | 6.04 | 2.05 | 2.33 | 2.31 | 4.61 | | BART* | 1.08 | 1.96 | 1.80 | 2.75 | 2.59 | 4.91 | 1.34 | 1.75 | 1.77 | 3.56 | | KeyBART-DOC* | 0.99 | 2.03 | 1.39 | 2.74 | 2.40 | 4.58 | 1.07 | 1.39 | 1.69 | 3.38 | | KeyBART* | 0.95 | 1.81 | 1.23 | 1.90 | 3.09 | 6.08 | 1.96 | 2.65 | 2.03 | 4.26 | | KeyBART* (Zero-shot) | 1.83 | 2.92 | 1.46 | 2.19 | 1.29 | 2.09 | 1.12 | 1.45 | 0.70 | 1.14 | | 9e0126a373cd4165d8f16d5bcec510f7 |
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