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 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 24 - 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 - training_steps: 2000 - mixed_precision_training: Native AMP | f1e19f1cbffd80f684b5ff5ec45169e0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.293 | 0.64 | 500 | 0.3798 | 99.9451 | | 0.1701 | 1.28 | 1000 | 0.3376 | 100.0 | | 0.1392 | 1.92 | 1500 | 0.3280 | 100.0 | | 0.0628 | 2.56 | 2000 | 0.3370 | 100.0 | | bd3a7e98d9c598b1c1146a4575fd7670 |
mit | ['generated_from_trainer'] | false | roberta-offensive-lm-tapt-finetuned This model is a fine-tuned version of [k4black/roberta-offensive-lm-tapt](https://huggingface.co/k4black/roberta-offensive-lm-tapt) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4692 - F1: 0.7744 | 72defc34b7713e670b8eaf4b392dcf99 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 12 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP | f54fa4319aa3a28eee60d9edf24f1aaf |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.6149 | 0.1 | 100 | 0.6323 | 0.3932 | | 0.5713 | 0.2 | 200 | 0.6223 | 0.5491 | | 0.5529 | 0.29 | 300 | 0.5739 | 0.6120 | | 0.5174 | 0.39 | 400 | 0.4812 | 0.7287 | | 0.5044 | 0.49 | 500 | 0.4667 | 0.7595 | | 0.5022 | 0.59 | 600 | 0.4540 | 0.7648 | | 0.4855 | 0.69 | 700 | 0.4523 | 0.7933 | | 0.465 | 0.78 | 800 | 0.4479 | 0.7727 | | 0.4591 | 0.88 | 900 | 0.4478 | 0.7914 | | 0.4702 | 0.98 | 1000 | 0.6035 | 0.7397 | | 0.4448 | 1.08 | 1100 | 0.4996 | 0.7535 | | 0.4476 | 1.18 | 1200 | 0.4692 | 0.7744 | | 14e7958b186d4e79b89c7e11ee29e5ac |
apache-2.0 | ['generated_from_keras_callback'] | false | kimhieu/distilbert-base-uncased-finetuned-cola 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: 0.1828 - Validation Loss: 0.5520 - Train Matthews Correlation: 0.5286 - Epoch: 2 | 71b953726f2d36d849395b16416261e6 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5184 | 0.4675 | 0.4484 | 0 | | 0.3164 | 0.4646 | 0.4963 | 1 | | 0.1828 | 0.5520 | 0.5286 | 2 | | 55e93c1d7ad2e6f25f6b429b2eae9f94 |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2085 - Accuracy: 0.9275 - F1: 0.9275 | 2a7df0a3d72842f4e3a62c05b74b5409 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8208 | 1.0 | 250 | 0.2989 | 0.9105 | 0.9085 | | 0.2418 | 2.0 | 500 | 0.2085 | 0.9275 | 0.9275 | | e0b7e5dc2501e79656998cdfe80d76ee |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_wavlm_s213 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (nl)](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. | bdbee591106af97194bde83bc4e77ee4 |
apache-2.0 | [] | false | MobileNet V2 model from Torchvision fine-tuned for FOOD101 dataset. Checkpoint trained for 30 epoches using https://github.com/AlexKoff88/mobilenetv2_food101. Top-1 accuracy is 76.3% but one can do better. The main intend is to use it in samples and demos for model optimization. Here is the advantages: - FOOD101 can automatically downloaded without registration and SMS. - It is quite representative to reflect the real world scenarios. - MobileNet v2 is easy to train and lightweight model which is also representative and used in many public benchmarks. Here is the code to load the checkpoint in PyTorch: ```python import sys import os import torch import torch.nn as nn import torchvision.models as models FOOD101_CLASSES = 101 def fix_names(state_dict): state_dict = {key.replace('module.', ''): value for (key, value) in state_dict.items()} return state_dict model = models.mobilenet_v2(num_classes=FOOD101_CLASSES) if len(sys.argv) > 1: checkpoint_path = sys.argv[1] if os.path.isfile(checkpoint_path): print("=> loading checkpoint '{}'".format(checkpoint_path)) checkpoint = torch.load(checkpoint_path) weights = fix_names(checkpoint['state_dict']) model.load_state_dict(weights) print("=> loaded checkpoint '{}' (epoch {})" .format(checkpoint_path, checkpoint['epoch'])) ``` | 3daed11b4d391e84ffdeb1d864c94c9e |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-06 - train_batch_size: 24 - eval_batch_size: 4 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 | 81f855f06559853dde58027a2da767a6 |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_unispeech-ml_s23 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (nl)](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. | 2542468aaddeeae2343aa5cc00f91e6d |
apache-2.0 | ['generated_from_trainer'] | false | distil-Is-upper 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: 0.6095 - Rmse: 0.7807 - Mse: 0.6095 - Mae: 0.5993 | 13a64d8aada2ef5bc7b8207a199eca4d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.7129 | 1.0 | 492 | 0.7088 | 0.8419 | 0.7088 | 0.5968 | | 0.5953 | 2.0 | 984 | 0.6426 | 0.8016 | 0.6426 | 0.5838 | | 0.5865 | 3.0 | 1476 | 0.6083 | 0.7800 | 0.6083 | 0.6023 | | 0.5888 | 4.0 | 1968 | 0.6209 | 0.7880 | 0.6209 | 0.5880 | | 0.5859 | 5.0 | 2460 | 0.6095 | 0.7807 | 0.6095 | 0.5993 | | b2fe58d29870c7d22b4700f4757c2f26 |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 4dfa2be4331d3d68f124aa5fd81f63217a7278a4 pip install -e . cd egs2/mini_librispeech/diar1 ./run.sh --skip_data_prep false --skip_train true --download_model YushiUeda/test ``` <!-- Generated by scripts/utils/show_diar_result.sh --> | 565f8b9c5706999b30324936906d896e |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | Environments - date: `Wed Aug 25 23:29:07 EDT 2021` - python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]` - espnet version: `espnet 0.10.2a1` - pytorch version: `pytorch 1.9.0+cu102` - Git hash: `19bcd34f9395e01e54a97c4db5ecbcedb429dd92` - Commit date: `Tue Aug 24 19:50:44 2021 -0400` | 350fd5428a3caa0ecc73a380dea7c15e |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | DER `dev_clean_2_ns2_beta2_500` |threshold_median_collar|DER| |---|---| |result_th0.3_med1_collar0.0|32.42| |result_th0.3_med11_collar0.0|32.03| |result_th0.4_med1_collar0.0|30.96| |result_th0.4_med11_collar0.0|30.26| |result_th0.5_med1_collar0.0|30.35| |result_th0.5_med11_collar0.0|29.37| |result_th0.6_med1_collar0.0|30.77| |result_th0.6_med11_collar0.0|29.52| |result_th0.7_med1_collar0.0|32.60| |result_th0.7_med11_collar0.0|31.03| | 0873c4d388dc51e1e58c63cf7473d41c |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | DIAR config <details><summary>expand</summary> ``` config: conf/train_diar.yaml print_config: false log_level: INFO dry_run: false iterator_type: chunk output_dir: exp/diar_train_diar_raw_max_epoch20 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: 20 patience: 3 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 3 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_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: 16 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/diar_stats_8k/train/speech_shape - exp/diar_stats_8k/train/spk_labels_shape valid_shape_file: - exp/diar_stats_8k/valid/speech_shape - exp/diar_stats_8k/valid/spk_labels_shape batch_type: folded valid_batch_type: null fold_length: - 80000 - 800 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 200000 chunk_shift_ratio: 0.5 num_cache_chunks: 64 train_data_path_and_name_and_type: - - dump/raw/simu/data/train_clean_5_ns2_beta2_500/wav.scp - speech - sound - - dump/raw/simu/data/train_clean_5_ns2_beta2_500/espnet_rttm - spk_labels - rttm valid_data_path_and_name_and_type: - - dump/raw/simu/data/dev_clean_2_ns2_beta2_500/wav.scp - speech - sound - - dump/raw/simu/data/dev_clean_2_ns2_beta2_500/espnet_rttm - spk_labels - rttm allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.01 scheduler: noamlr scheduler_conf: warmup_steps: 1000 num_spk: 2 init: xavier_uniform input_size: null model_conf: loss_type: pit use_preprocessor: true frontend: default frontend_conf: fs: 8k hop_length: 128 normalize: global_mvn normalize_conf: stats_file: exp/diar_stats_8k/train/feats_stats.npz encoder: transformer encoder_conf: input_layer: linear num_blocks: 2 linear_units: 512 dropout_rate: 0.1 output_size: 256 attention_heads: 4 attention_dropout_rate: 0.0 decoder: linear decoder_conf: {} label_aggregator: label_aggregator label_aggregator_conf: {} required: - output_dir version: 0.10.2a1 distributed: false ``` </details> | a8facf54aae769f029a3517c0f60ec64 |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2201 - Accuracy: 0.9275 - F1: 0.9275 | 98f621bcc1846fc6a0da1146eec00127 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8326 | 1.0 | 250 | 0.3185 | 0.902 | 0.8983 | | 0.2499 | 2.0 | 500 | 0.2201 | 0.9275 | 0.9275 | | 7e5d48009698b0435c4f6737e9612008 |
creativeml-openrail-m | ['text-to-image'] | false | kemar Dreambooth model trained by zigg-ai with 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: sdcid (use that on your prompt)  | e1c6d64ac10a85254846a191ff9d90d1 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `pyf98/swbd_e_branchformer` This model was trained by Yifan Peng using swbd recipe in [espnet](https://github.com/espnet/espnet/). References: - [E-Branchformer: Branchformer with Enhanced merging for speech recognition (SLT 2022)](https://arxiv.org/abs/2210.00077) - [Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding (ICML 2022)](https://proceedings.mlr.press/v162/peng22a.html) | 6c560c261b47c8c980f0539c989e3de6 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout ee573bc6f5de4309c1e29137294a7305d9175e65 pip install -e . cd egs2/swbd/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model pyf98/swbd_e_branchformer ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 2985cdddb6e6f87e28d6976cf5364387 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Tue Dec 27 05:05:40 CST 2022` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.12.1` - Git hash: `ef3ce328551c12c03284defc757f42df47c46170` - Commit date: `Mon Dec 26 20:34:28 2022 -0500` | a668ad4527630e50170984ce9f4f724e |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/eval2000/hyp.callhm.ctm.filt.sys|2628|21594|88.7|8.4|2.9|2.1|13.4|46.2| |decode_asr_asr_model_valid.acc.ave/eval2000/hyp.ctm.filt.sys|4459|42989|91.2|6.1|2.8|1.5|10.4|41.5| |decode_asr_asr_model_valid.acc.ave/eval2000/hyp.swbd.ctm.filt.sys|1831|21395|93.7|3.7|2.6|1.0|7.3|34.8| | f4b30a8f59891f9e4fa7e1ded0df2c61 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_e_branchformer_e12_size256_mlp1024_linear1024_macaron.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_e_branchformer_e12_size256_mlp1024_linear1024_macaron_raw_en_bpe2000_sp ngpu: 1 seed: 2022 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 2 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 37983 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 100 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: true log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false 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: 20 valid_batch_size: null batch_bins: 40000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe2000_sp/train/speech_shape - exp/asr_stats_raw_en_bpe2000_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe2000_sp/valid/speech_shape - exp/asr_stats_raw_en_bpe2000_sp/valid/text_shape.bpe batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_nodup_sp/wav.scp - speech - kaldi_ark - - dump/raw/train_nodup_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/train_dev/wav.scp - speech - kaldi_ark - - dump/raw/train_dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.002 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 50000 token_list: - <blank> - <unk> - ▁i - '''' - s - ▁and - ▁the - ▁you - ▁that - ▁a - ▁it - ▁uh - ▁to - t - ▁of - ▁know - ▁they - '-' - ▁in - ▁we - ']' - ▁[ - ▁yeah - ▁have - ▁but - ▁so - ▁was - ▁like - re - ▁um - ▁just - ▁well - ▁do - m - ▁for - ing - ▁think - ▁don - d - ▁is - ▁there - ▁or - ▁on - ▁be - noise - ▁what - ▁oh - laughter - ▁my - ed - ve - ▁not - ▁really - ▁with - ▁he - n - ▁one - ▁if - ▁are - ▁all - ▁get - ▁right - ▁about - ▁can - ▁because - ▁out - ▁had - ▁up - ▁them - ▁lot - ▁at - ▁this - ▁would - ▁when - ▁go - ▁some - er - ▁people - ▁no - ▁mean - ▁kind - ▁then - a - v - ▁good - e - ll - ▁now - ▁got - ▁me - p - ▁time - o - ▁she - ▁as - ▁going - y - ▁see - ▁more - ▁were - ly - ▁been - ▁from - ▁too - ▁an - ▁things - ▁how - ▁something - ▁your - ▁where - ▁much - ▁guess - c - r - ▁little - ▁here - ▁s - ▁thing - ▁our - u - g - ocalized - ▁very - ▁did - b - ▁their - ▁other - ▁work - le - ▁could - ▁okay - i - ▁even - al - ▁c - ▁two - huh - ▁way - ▁say - or - in - ▁any - ▁has - ▁years - ▁want - ▁t - f - ▁back - ▁down - ▁those - ▁pretty - ▁probably - ▁re - ▁who - ▁home - ▁didn - ▁real - ▁year - ▁take - ▁over - ▁yes - ▁than - ▁sure - ▁into - ar - hum - an - l - ▁school - ▁put - ▁stuff - k - ▁make - ▁kids - ▁her - ▁said - ▁by - ▁never - ▁which - ▁off - w - ▁went - ▁b - ▁car - ▁only - ion - ▁big - ▁always - ▁around - ▁money - ▁these - ▁day - ▁anything - ▁three - ▁nice - ▁doing - ri - ▁need - ▁come - ▁f - ▁actually - ▁will - ▁maybe - ▁care - ▁him - ▁de - ent - ▁still - ▁v - ▁should - ▁new - ▁used - ch - ▁five - ▁ - th - ▁long - ▁p - ▁sort - ▁e - ▁his - ter - 'on' - ▁most - ▁house - ▁bit - ▁old - ▁every - ▁different - ck - ▁last - ▁let - ▁use - il - ▁us - ▁many - ▁look - es - ▁course - ▁getting - ur - ▁true - ▁everything - ic - ▁feel - ▁first - ▁part - ▁does - ▁pay - ▁great - it - ▁hard - ▁same - ▁thought - en - ▁problem - ▁also - ▁keep - at - ▁d - ers - ▁through - ▁o - ▁doesn - ies - ▁children - ▁four - ▁find - ▁done - ▁th - ment - ▁before - ▁far - ▁though - ▁area - ate - ▁haven - ▁w - ▁ever - ▁being - li - ▁family - ▁bad - ▁seems - se - ▁live - ation - ▁whole - ▁fact - ▁own - ▁n - ▁why - ▁huh - ▁play - ▁talking - ▁tell - ▁better - ▁interesting - ▁another - h - ▁place - ▁try - ▁trying - ro - ▁ten - ▁twenty - ▁else - ol - ▁watch - ▁read - te - ▁type - ▁quite - ▁job - ir - ▁hundred - ▁high - ▁call - ▁after - ow - ▁ago - ▁give - ra - ▁couple - ▁enough - us - ▁whatever - ke - ▁either - ▁start - ▁m - ▁having - ▁texas - ▁somebody - el - ▁husband - ▁sometimes - ▁dollars - ▁usually - ▁show - ▁help - ce - ▁while - ▁few - ▁away - ▁y - ▁ha - ▁se - ▁college - ▁system - able - ▁might - ▁ma - ▁heard - ▁r - ne - is - ▁person - ▁once - ▁made - ▁point - ▁six - ▁fun - ▁g - ▁week - ▁buy - ▁seen - ▁state - ▁anyway - ▁again - ▁pa - ▁love - ▁gonna - ▁dallas - ▁started - ▁pro - ▁exactly - ▁country - ▁life - ▁enjoy - ▁everybody - ive - id - ▁talk - ▁night - ▁able - est - ▁lo - ▁may - ▁stay - ▁remember - ▁news - ▁mo - um - ▁came - ▁co - ▁hear - et - ▁end - ▁least - tion - ▁working - ▁h - lo - ▁un - ▁sa - un - ▁po - ul - ▁boy - ▁since - age - ▁change - ▁di - ▁idea - ▁both - 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▁stupid - ▁voice - ▁pump - ▁independent - ▁practice - ▁tomatoes - ▁blame - ▁consumer - ▁outdoor - ▁northern - ▁craft - ▁antonio - ▁republic - ▁written - ▁tennis - ▁tune - ology - ▁legislat - ▁finance - ▁adjust - ▁massachusetts - ▁successful - ▁repeat - ▁chemical - ▁versus - ▁milk - ▁carpet - ▁horse - ▁address - ▁speed - ▁media - ▁apart - ▁occasion - ▁belong - ▁francisco - ▁grandchildren - ▁whoever - ▁quiet - ▁shirt - ▁knee - izing - ▁register - ▁holiday - ▁resource - ▁mechanic - ▁receive - ▁staff - ▁steal - ▁maintain - ▁toyota - ▁psych - ▁casual - ▁backyard - ▁chose - ▁author - ▁energy - ▁bread - ▁focus - ▁journal - ▁professor - ▁sentencing - ▁explain - ▁knock - ficial - ▁amazed - ▁baltimore - ▁facilities - ▁neither - ▁potato - ▁advance - ▁sweet - ▁gulf - hold - ▁candidate - ▁pittsburgh - ▁garland - ▁babies - ▁hung - ▁involve - ▁spec - ▁concept - ▁convince - ▁impressed - ▁leaving - ▁primarily - ▁produce - ▁victim - ▁herself - ▁shock - ▁juries - ▁loose - ▁strip - wood - ▁represent - ▁georgia - ▁kindergarten - ▁progress - ▁yellow - ▁stock - ▁junk - ▁robb - ▁surprise - ▁circumstances - ▁dangerous - ▁illegal - ▁concert - ▁shift - ▁moral - ▁disappoint - ▁advertise - ▁educate - ▁female - ▁minimum - ▁establish - ▁fantastic - ▁welfare - house - ▁birthday - ▁cruise - ▁culture - ▁elementary - ▁employer - ▁incentive - ▁relationship - ▁speech - ▁reduce - ▁original - ▁august - ▁grandparents - ▁preschool - ▁violent - ▁barbecue - ▁fifties - ▁rabbit - ▁freedom - ▁parole - ▁attract - ▁fascinat - ▁innocent - ▁perspective - ▁temperature - ▁emotion - ▁pollut - ▁negative - ▁wisconsin - ▁contact - ▁impact - ▁jersey - ▁recognize - ▁conscious - ▁detail - ▁complete - ▁creek - ▁attack - ▁claim - ▁continu - ▁attorney - ▁campaign - ▁conservative - ▁enforce - ▁excited - ▁canada - ▁multi - ▁audi - ▁challenge - ▁evidence - ▁maintenance - ▁pepper - ▁release - ▁frame - employed - ▁include - ▁paycheck - ▁raleigh - ▁religious - ▁semester - '1' - '4' - '2' - '&' - '6' - '8' - '9' - '7' - '5' - / - q - '3' - '[' - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: null use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram2000/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' short_noise_thres: 0.5 frontend: default frontend_conf: n_fft: 512 win_length: 400 hop_length: 160 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 5 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_bpe2000_sp/train/feats_stats.npz model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false preencoder: null preencoder_conf: {} encoder: e_branchformer encoder_conf: output_size: 256 attention_heads: 4 attention_layer_type: rel_selfattn pos_enc_layer_type: rel_pos rel_pos_type: latest cgmlp_linear_units: 1024 cgmlp_conv_kernel: 31 use_linear_after_conv: false gate_activation: identity num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d layer_drop_rate: 0.0 linear_units: 1024 positionwise_layer_type: linear use_ffn: true macaron_ffn: true merge_conv_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 preprocessor: default preprocessor_conf: {} required: - output_dir - token_list version: '202211' distributed: true ``` </details> | 39bfea4f334437b9308b40b69e861242 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-vanilla-target-glue-cola-linear-probe This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6182 - Matthews Correlation: 0.0 | 35f1f3113f78a795ed64f40119c1e270 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6219 | 1.87 | 500 | 0.6194 | 0.0 | | 0.6094 | 3.73 | 1000 | 0.6188 | 0.0 | | 0.6086 | 5.6 | 1500 | 0.6183 | 0.0 | | 0.6079 | 7.46 | 2000 | 0.6182 | 0.0 | | c4c0e9ab096278da9822fa1aa1b4da73 |
apache-2.0 | ['generated_from_trainer'] | false | juancopi81/whisper-medium-es-train-valid This model is a fine-tuned version of [juancopi81/whisper-medium-es-train-valid](https://huggingface.co/juancopi81/whisper-medium-es-train-valid) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2227 - Wer: 6.1548 Using the script provided in the Whisper Sprint (Dec. 2022) the models achieves these results on the evaluation sets (WER): - google/fleurs: 6.94 - mozilla-foundation/common_voice_11_0: XXXX | dfbe5df8df20a652a9f51c9e2b96dc0d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0539 | 1.01 | 1000 | 0.2100 | 6.4465 | | 0.0211 | 2.01 | 2000 | 0.2286 | 6.5082 | | 0.0088 | 3.02 | 3000 | 0.2418 | 6.3848 | | 0.0205 | 4.02 | 4000 | 0.2288 | 6.6603 | | 0.1031 | 5.03 | 5000 | 0.2227 | 6.1548 | | e2fd3c9d1854d03f078fcf06343499f7 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-irish-colab 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: 1.4286 - Wer: 0.5097 | 2d2c800e36ff9ff8a147ce1cf067fbba |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 16 - 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: 500 - num_epochs: 210 - mixed_precision_training: Native AMP | 4d05e0954b5922bbe36735016537ae9a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 4.3406 | 24.97 | 400 | 1.1677 | 0.7270 | | 0.2527 | 49.97 | 800 | 1.2686 | 0.5927 | | 0.0797 | 74.97 | 1200 | 1.3970 | 0.5769 | | 0.0424 | 99.97 | 1600 | 1.4093 | 0.5600 | | 0.0286 | 124.97 | 2000 | 1.3684 | 0.5407 | | 0.0174 | 149.97 | 2400 | 1.4571 | 0.5205 | | 0.0109 | 174.97 | 2800 | 1.4327 | 0.5178 | | 0.0072 | 199.97 | 3200 | 1.4286 | 0.5097 | | 74079b23f95c6497e2d7eaeb585965f4 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Turkish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | e97cf572079e73029505c06b86574ae7 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "tr", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("ozcangundes/wav2vec2-large-xlsr-53-turkish") model = Wav2Vec2ForCTC.from_pretrained("ozcangundes/wav2vec2-large-xlsr-53-turkish") resampler = torchaudio.transforms.Resample(48_000, 16_000) | 059fddb2114d2d4dc34f6fa9eddd1c2e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): \\tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \\tbatch["speech"] = resampler(speech_array).squeeze().numpy() \\treturn batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): \\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` | 4ec7bb28af17c1007454f88f704e40c8 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Turkish test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "tr", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("ozcangundes/wav2vec2-large-xlsr-53-turkish") model = Wav2Vec2ForCTC.from_pretrained("ozcangundes/wav2vec2-large-xlsr-53-turkish") model.to("cuda") chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\’\\']' resampler = torchaudio.transforms.Resample(48_000, 16_000) | 11037556217c2b999edb23c24aa0b460 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) | 50a1eebb51f072144a0e3a0cc8c7ffd3 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 29.62 % | 7ec859327ea088713d9622c793d56c9b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice `train` and `validation` datasets were used for training. The script used for training can be found [here](https://colab.research.google.com/drive/1hesw9z_kFFINT93jBvGuFspOLrHx10AE?usp=sharing) | 3cb296911e9ced6ca013bfd9c9d746f7 |
mit | [] | false | nixeu on Stable Diffusion This is the `<nixeu>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).  Here is the new concept you will be able to use as a `style`:       | c0ae43391282a151ad5d28320fd05258 |
mit | [] | false | model by Pinguin This your the Stable Diffusion model fine-tuned the a hat in time girl concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a render of sks ** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). Here are the images used for training this concept:     | df24d8ca5cd9af98ec69be75c6a5bcea |
mit | [] | false | Spanish truecasing model This is a Spanish truecasing-model that works with the <b>Dalton Fury</b> Python project: https://github.com/daltonfury42/truecase You can install it here: https://pypi.org/project/truecase/ | 2890e336246172e0ab72c1bfc3197281 |
mit | [] | false | Quick start To use the Spanish model use the TrueCase.py file uploaded to this repository https://huggingface.co/HURIDOCS/spanish_truecasing/blob/main/TrueCaser.py Install the requirements: pip install nltk And ready to work: from TrueCaser import TrueCaser model_path = "spanish.dist" spanish_truecasing = TrueCaser(model_path) text = 'informe no.78/08. petición 785-05 admisibilidad. vicente arturo villanueva ortega y otros.' print(spanish_truecasing.get_true_case(text)) | ec79f29654493b97498836e9f3cacd22 |
mit | [] | false | Notes The model was trained with the Europarl dataset that contains transcriptions of the European Parliament discusions: https://www.statmt.org/europarl/ Europarl: A Parallel Corpus for Statistical Machine Translation, Philipp Koehn, MT Summit 2005 Using huggingface load_dataset: europarl = load_dataset('large_spanish_corpus', name='Europarl') | 20521dbf2da1d75fc919ae8f3fb55cdd |
apache-2.0 | [] | false | Arabic T5 Small Model A customized T5 Model for Arabic and English Task. It could be used as an alternative for `google/mt5-small` model, as it's much smaller and only targets Arabic and English based tasks. | f91f81fc04660d9e2ee6a03badfcba74 |
apache-2.0 | [] | false | About T5 ``` T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format. The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. ``` [Read More](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) | bfc55e9c8478ac94573219975e1c8f67 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-qnli-custom-tokenizer-expand-vocab 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: 3.0716 | 43a746e7d8f1d866b5318284e9edb909 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.5339 | 0.4 | 500 | 4.7224 | | 4.6477 | 0.8 | 1000 | 4.3242 | | 4.3146 | 1.2 | 1500 | 3.9988 | | 4.0046 | 1.6 | 2000 | 3.7777 | | 3.7942 | 2.0 | 2500 | 3.5976 | | 3.5684 | 2.4 | 3000 | 3.4426 | | 3.4406 | 2.8 | 3500 | 3.3275 | | 3.332 | 3.2 | 4000 | 3.2361 | | 3.1941 | 3.6 | 4500 | 3.1616 | | 3.0981 | 4.0 | 5000 | 3.0716 | | 09f4aea6be15a46284e0cab64500e279 |
cc-by-sa-4.0 | [] | false | Example *Use `Diffusers` >=0.8.0, do not support lower versions.* ```python from diffusers import StableDiffusionPipeline import torch model_path = "foldl/sd-rumeme-desc" pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16) pipe.to("cuda") image = pipe(prompt="кот").images[0] image.save("cat".jpg) ``` | f23c9bd6f65fc75f2a0ff966b82ae728 |
cc-by-sa-4.0 | [] | false | Training Procedure Model was trained on 1 P100 GPU for 10k steps. Base model - https://huggingface.co/OFA-Sys/small-stable-diffusion-v0 Training notebook here - https://www.kaggle.com/code/nukeee/meme-diffusion | b5f3516371f12dab24b46367206d4a23 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_vp-100k_gender_male-8_female-2_s226 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 (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. | 61ea51dbaff27ca895c036d0c58cf84a |
apache-2.0 | [] | false | Graphcore/gpt2-small-ipu Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore). Through HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project. | b87197aa379214507e09c339cf55fbce |
apache-2.0 | [] | false | Intended uses & limitations This model contains just the `IPUConfig` files for running the [GPT2 Small](https://huggingface.co/gpt2) model on Graphcore IPUs. **This model contains no model weights, only an IPUConfig.** | d885ecf56e8dc3bb5453b92e3eb77891 |
apache-2.0 | ['generated_from_keras_callback'] | false | mzchua/distilbert-base-uncased-finetuned-cola 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: 0.1940 - Validation Loss: 0.4943 - Train Matthews Correlation: 0.5481 - Epoch: 2 | 89081eb899a0eef1445aebc7f5c3241b |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5156 | 0.4940 | 0.3942 | 0 | | 0.3226 | 0.4322 | 0.5448 | 1 | | 0.1940 | 0.4943 | 0.5481 | 2 | | d6a7f530e5f56b7721a9efebb9a7cba3 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-custom-tokenizer-target-glue-sst2 This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola-custom-tokenizer](https://huggingface.co/muhtasham/tiny-mlm-glue-cola-custom-tokenizer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4535 - Accuracy: 0.7959 | fec5042768982bf5a51d4d72c36a877c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6748 | 0.24 | 500 | 0.6424 | 0.6468 | | 0.5996 | 0.48 | 1000 | 0.5542 | 0.7167 | | 0.5172 | 0.71 | 1500 | 0.5001 | 0.7511 | | 0.4835 | 0.95 | 2000 | 0.4613 | 0.7741 | | 0.4366 | 1.19 | 2500 | 0.4602 | 0.7901 | | 0.4127 | 1.43 | 3000 | 0.4334 | 0.8028 | | 0.3894 | 1.66 | 3500 | 0.4507 | 0.7867 | | 0.3732 | 1.9 | 4000 | 0.4305 | 0.8154 | | 0.3646 | 2.14 | 4500 | 0.4369 | 0.8085 | | 0.3417 | 2.38 | 5000 | 0.4589 | 0.7947 | | 0.3343 | 2.61 | 5500 | 0.4535 | 0.7959 | | 40d31e9603fbbdebbf9ab420854dcd05 |
apache-2.0 | ['generated_from_trainer'] | false | distilBERT-finetuned-resumes-sections This model is a fine-tuned version of [Geotrend/distilbert-base-en-fr-cased](https://huggingface.co/Geotrend/distilbert-base-en-fr-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0369 - F1: 0.9652 - Roc Auc: 0.9808 - Accuracy: 0.9621 | c85ef35a4f365346f5ddbe2723645f10 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:| | 0.0509 | 1.0 | 1173 | 0.0331 | 0.9439 | 0.9659 | 0.9356 | | 0.024 | 2.0 | 2346 | 0.0274 | 0.9550 | 0.9750 | 0.9493 | | 0.0148 | 3.0 | 3519 | 0.0290 | 0.9493 | 0.9712 | 0.9446 | | 0.0089 | 4.0 | 4692 | 0.0324 | 0.9492 | 0.9714 | 0.9442 | | 0.0071 | 5.0 | 5865 | 0.0317 | 0.9540 | 0.9732 | 0.9476 | | 0.0064 | 6.0 | 7038 | 0.0324 | 0.9527 | 0.9742 | 0.9484 | | 0.0036 | 7.0 | 8211 | 0.0320 | 0.9574 | 0.9766 | 0.9540 | | 0.0042 | 8.0 | 9384 | 0.0367 | 0.9528 | 0.9732 | 0.9493 | | 0.0052 | 9.0 | 10557 | 0.0342 | 0.9563 | 0.9757 | 0.9531 | | 0.0027 | 10.0 | 11730 | 0.0294 | 0.9629 | 0.9800 | 0.9595 | | 0.0017 | 11.0 | 12903 | 0.0355 | 0.9605 | 0.9778 | 0.9582 | | 0.0022 | 12.0 | 14076 | 0.0338 | 0.9627 | 0.9792 | 0.9591 | | 0.0012 | 13.0 | 15249 | 0.0358 | 0.9609 | 0.9780 | 0.9591 | | 0.0011 | 14.0 | 16422 | 0.0360 | 0.9618 | 0.9791 | 0.9604 | | 0.0009 | 15.0 | 17595 | 0.0358 | 0.9648 | 0.9807 | 0.9625 | | 0.0007 | 16.0 | 18768 | 0.0373 | 0.9627 | 0.9794 | 0.9595 | | 0.0006 | 17.0 | 19941 | 0.0397 | 0.9597 | 0.9774 | 0.9574 | | 0.0008 | 18.0 | 21114 | 0.0369 | 0.9652 | 0.9808 | 0.9621 | | 0.0007 | 19.0 | 22287 | 0.0377 | 0.9646 | 0.9801 | 0.9621 | | 0.0005 | 20.0 | 23460 | 0.0381 | 0.9639 | 0.9797 | 0.9616 | | 82a27138327bf7141e1202429aa7d422 |
mit | ['generated_from_trainer'] | false | hmBERT-CoNLL-cp3 This model is a fine-tuned version of [dbmdz/bert-base-historic-multilingual-cased](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0572 - Precision: 0.9121 - Recall: 0.9243 - F1: 0.9182 - Accuracy: 0.9862 | c7b4d36a3b02f5952347c4e84cf3e396 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.06 | 25 | 0.4115 | 0.3643 | 0.3728 | 0.3685 | 0.9007 | | No log | 0.11 | 50 | 0.2243 | 0.6393 | 0.6908 | 0.6641 | 0.9460 | | No log | 0.17 | 75 | 0.1617 | 0.7319 | 0.7637 | 0.7475 | 0.9580 | | No log | 0.23 | 100 | 0.1544 | 0.7282 | 0.7637 | 0.7455 | 0.9585 | | No log | 0.28 | 125 | 0.1341 | 0.7595 | 0.8117 | 0.7847 | 0.9644 | | No log | 0.34 | 150 | 0.1221 | 0.7980 | 0.8251 | 0.8114 | 0.9693 | | No log | 0.4 | 175 | 0.1013 | 0.7968 | 0.8344 | 0.8152 | 0.9719 | | No log | 0.46 | 200 | 0.1076 | 0.8265 | 0.8403 | 0.8333 | 0.9732 | | No log | 0.51 | 225 | 0.0883 | 0.8453 | 0.8635 | 0.8543 | 0.9763 | | No log | 0.57 | 250 | 0.0973 | 0.8439 | 0.8633 | 0.8535 | 0.9763 | | No log | 0.63 | 275 | 0.0883 | 0.8497 | 0.8655 | 0.8575 | 0.9765 | | No log | 0.68 | 300 | 0.0879 | 0.8462 | 0.8642 | 0.8551 | 0.9766 | | No log | 0.74 | 325 | 0.0781 | 0.8592 | 0.8834 | 0.8711 | 0.9787 | | No log | 0.8 | 350 | 0.0725 | 0.8697 | 0.8928 | 0.8811 | 0.9803 | | No log | 0.85 | 375 | 0.0755 | 0.8687 | 0.8943 | 0.8813 | 0.9807 | | No log | 0.91 | 400 | 0.0666 | 0.8781 | 0.9004 | 0.8891 | 0.9822 | | No log | 0.97 | 425 | 0.0658 | 0.8877 | 0.8995 | 0.8936 | 0.9823 | | No log | 1.03 | 450 | 0.0645 | 0.8951 | 0.9036 | 0.8993 | 0.9837 | | No log | 1.08 | 475 | 0.0697 | 0.8864 | 0.9039 | 0.8951 | 0.9831 | | 0.1392 | 1.14 | 500 | 0.0688 | 0.8824 | 0.8994 | 0.8908 | 0.9824 | | 0.1392 | 1.2 | 525 | 0.0681 | 0.8950 | 0.9049 | 0.8999 | 0.9827 | | 0.1392 | 1.25 | 550 | 0.0676 | 0.8855 | 0.8977 | 0.8915 | 0.9823 | | 0.1392 | 1.31 | 575 | 0.0618 | 0.8940 | 0.9088 | 0.9014 | 0.9842 | | 0.1392 | 1.37 | 600 | 0.0644 | 0.8945 | 0.9076 | 0.9010 | 0.9840 | | 0.1392 | 1.42 | 625 | 0.0641 | 0.8936 | 0.9086 | 0.9010 | 0.9837 | | 0.1392 | 1.48 | 650 | 0.0619 | 0.8969 | 0.9120 | 0.9044 | 0.9846 | | 0.1392 | 1.54 | 675 | 0.0608 | 0.9045 | 0.9105 | 0.9075 | 0.9848 | | 0.1392 | 1.59 | 700 | 0.0624 | 0.9038 | 0.9143 | 0.9091 | 0.9851 | | 0.1392 | 1.65 | 725 | 0.0596 | 0.9062 | 0.9170 | 0.9116 | 0.9852 | | 0.1392 | 1.71 | 750 | 0.0580 | 0.8995 | 0.9143 | 0.9069 | 0.9848 | | 0.1392 | 1.77 | 775 | 0.0582 | 0.9082 | 0.9172 | 0.9127 | 0.9858 | | 0.1392 | 1.82 | 800 | 0.0588 | 0.9024 | 0.9179 | 0.9101 | 0.9852 | | 0.1392 | 1.88 | 825 | 0.0592 | 0.9020 | 0.9219 | 0.9119 | 0.9856 | | 0.1392 | 1.94 | 850 | 0.0600 | 0.9054 | 0.9182 | 0.9118 | 0.9852 | | 0.1392 | 1.99 | 875 | 0.0568 | 0.9068 | 0.9202 | 0.9135 | 0.9861 | | 0.1392 | 2.05 | 900 | 0.0571 | 0.9131 | 0.9212 | 0.9171 | 0.9861 | | 0.1392 | 2.11 | 925 | 0.0577 | 0.9110 | 0.9204 | 0.9157 | 0.9858 | | 0.1392 | 2.16 | 950 | 0.0605 | 0.9127 | 0.9243 | 0.9185 | 0.9860 | | 0.1392 | 2.22 | 975 | 0.0575 | 0.9109 | 0.9224 | 0.9166 | 0.9867 | | 0.0392 | 2.28 | 1000 | 0.0572 | 0.9121 | 0.9243 | 0.9182 | 0.9862 | | 0.0392 | 2.33 | 1025 | 0.0567 | 0.9171 | 0.9253 | 0.9212 | 0.9870 | | 0.0392 | 2.39 | 1050 | 0.0570 | 0.9193 | 0.9295 | 0.9244 | 0.9871 | | 0.0392 | 2.45 | 1075 | 0.0584 | 0.9155 | 0.9276 | 0.9215 | 0.9867 | | 0.0392 | 2.51 | 1100 | 0.0591 | 0.9168 | 0.9286 | 0.9227 | 0.9867 | | 0.0392 | 2.56 | 1125 | 0.0577 | 0.9182 | 0.9312 | 0.9246 | 0.9874 | | 0.0392 | 2.62 | 1150 | 0.0570 | 0.9184 | 0.9283 | 0.9233 | 0.9870 | | 0.0392 | 2.68 | 1175 | 0.0563 | 0.9191 | 0.9298 | 0.9245 | 0.9872 | | 0.0392 | 2.73 | 1200 | 0.0565 | 0.9180 | 0.9313 | 0.9246 | 0.9872 | | 0.0392 | 2.79 | 1225 | 0.0559 | 0.9190 | 0.9298 | 0.9244 | 0.9873 | | 0.0392 | 2.85 | 1250 | 0.0562 | 0.9185 | 0.9293 | 0.9239 | 0.9873 | | 0.0392 | 2.9 | 1275 | 0.0564 | 0.9175 | 0.9285 | 0.9230 | 0.9872 | | 0.0392 | 2.96 | 1300 | 0.0563 | 0.9181 | 0.9295 | 0.9237 | 0.9873 | | 7507680b6901aa6c12b01bd7256b8bce |
mit | ['generated_from_trainer'] | false | indobert-hoax-classification This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6230 - Accuracy: 0.8059 | 6d8d0240542b8d42382892dc7b55762c |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.2173070213315e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 30 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | af1eb8e200ac1a34af7a8904000a7c07 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 85 | 0.5540 | 0.7029 | | No log | 2.0 | 170 | 0.5432 | 0.7029 | | No log | 3.0 | 255 | 0.4963 | 0.7441 | | No log | 4.0 | 340 | 0.5791 | 0.7971 | | No log | 5.0 | 425 | 0.6230 | 0.8059 | | 78ccdc0c172eb2222aa4f3c968b79dc6 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the mimica concept trained by mjfang27 on the mjfang27/dreambooth-hackathon-images dataset. This is a Stable Diffusion model fine-tuned on the mimica concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of mimica cat** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! | e5dba81d507580a3c12199c7183d2a6b |
cc-by-4.0 | ['question-answering, multi-step-reasoning, multi-hop-reasoning'] | false | digit_tokenization.py from https://github.com/stonybrooknlp/teabreac model_name = "StonyBrookNLP/teabreac-bart-large-iirc-retrieved" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) | 5ed880e960365f601887f581a831bff6 |
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: 0.2202 - Accuracy: 0.925 - F1: 0.9252 | 4223182de4d87f0c4fb1443589b93df1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8419 | 1.0 | 250 | 0.3236 | 0.9025 | 0.8999 | | 0.258 | 2.0 | 500 | 0.2202 | 0.925 | 0.9252 | | 6305af9f0b89a9d8a34e2074b9ab337d |
mit | [] | false | agm-style on Stable Diffusion Artist: <https://www.pixiv.net/en/users/20670939> This is the `<agm-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:       | a1bd3ea0c46de7c0828ceace9396be99 |
apache-2.0 | ['generated_from_keras_callback'] | false | KakkiDaisuki/bert-finetuned-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0259 - Validation Loss: 0.0580 - Epoch: 2 | f94cf19f0bbedc1d056c495e2b88e6f0 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1253 | 0.0569 | 0 | | 0.0417 | 0.0582 | 1 | | 0.0259 | 0.0580 | 2 | | 643efeec3b894d465bf6aee895e53352 |
apache-2.0 | ['generated_from_trainer'] | false | Article_500v8_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v8_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2113 - Precision: 0.7349 - Recall: 0.7560 - F1: 0.7453 - Accuracy: 0.9421 | ac4c6276dda8877eadee8f1d7381a481 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 191 | 0.1914 | 0.7105 | 0.7181 | 0.7143 | 0.9382 | | No log | 2.0 | 382 | 0.2045 | 0.7283 | 0.7574 | 0.7426 | 0.9408 | | 0.1441 | 3.0 | 573 | 0.2113 | 0.7349 | 0.7560 | 0.7453 | 0.9421 | | efa7bd7978751ef105a5add9bb76c50c |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Whisper Large French Cased This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the mozilla-foundation/common_voice_11_0 fr dataset. It achieves the following results on the evaluation set: - Loss: 0.2962 - Wer: 11.9100 | ce0ec531906f0f8a39330dc96eac27a4 |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP | bcf5d2d1cb3583b8605161adadbe9412 |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3357 | 0.2 | 1000 | 0.3994 | 16.1523 | | 0.3026 | 0.4 | 2000 | 0.3802 | 15.2403 | | 0.2904 | 0.6 | 3000 | 0.3389 | 14.0045 | | 0.2407 | 0.8 | 4000 | 0.3135 | 12.7947 | | 0.2451 | 1.0 | 5000 | 0.2962 | 11.9100 | | f9e938789642f6d9f6af6abd2392bb30 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_wnli_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3436 - Accuracy: 0.5634 | 77ab6478ea8eb8aa5277381f8ebd603a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3511 | 1.0 | 3 | 0.3436 | 0.5634 | | 0.3479 | 2.0 | 6 | 0.3457 | 0.5634 | | 0.3474 | 3.0 | 9 | 0.3462 | 0.5634 | | 0.3477 | 4.0 | 12 | 0.3442 | 0.5634 | | 0.3486 | 5.0 | 15 | 0.3442 | 0.5634 | | 0.3479 | 6.0 | 18 | 0.3455 | 0.5634 | | 654ebf2d5f414ff07178812d588b7604 |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-ner This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0492 - Precision: 0.9530 - Recall: 0.9604 - F1: 0.9567 - Accuracy: 0.9889 | cf3ca54a394fa9aa9ce9f9352c3fdee9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2031 | 1.0 | 878 | 0.0560 | 0.9381 | 0.9445 | 0.9413 | 0.9858 | | 0.0446 | 2.0 | 1756 | 0.0480 | 0.9510 | 0.9578 | 0.9544 | 0.9887 | | 0.0263 | 3.0 | 2634 | 0.0492 | 0.9530 | 0.9604 | 0.9567 | 0.9889 | | 7aa8c46cd917539d80d418b92ca8b6fd |
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: 0.0858 - Precition: 0.9363 - Recall: 0.9522 - F1: 0.9442 - Accuracy: 0.9866 | 7a24e0d995ec0ef17b3fc545118822ce |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precition | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0081 | 1.0 | 1756 | 0.0914 | 0.9273 | 0.9446 | 0.9359 | 0.9848 | | 0.012 | 2.0 | 3512 | 0.0852 | 0.9321 | 0.9478 | 0.9399 | 0.9857 | | 0.0036 | 3.0 | 5268 | 0.0858 | 0.9363 | 0.9522 | 0.9442 | 0.9866 | | a341161159babb9841004bd08b8028dc |
mit | ['generated_from_trainer'] | false | hungry_saha This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. | 54a23d5d8c57146f49ffe4bdc8d2909e |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.00056}, 'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257], [50258]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048, 'prefix': '<|aligned|>'}, {'generate_kwargs': {'bad_words_ids': [[50257], [50258]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prefix': '<|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'hungry_saha', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | 197ae1170c013251588c01ce2a12ea0b |
apache-2.0 | ['generated_from_keras_callback'] | false | prahlad/rotten_model This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on rotten_tomatoes movie review dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4876 - Train Accuracy: 0.7620 - Validation Loss: 0.5001 - Validation Accuracy: 0.7842 - Epoch: 0 | d613ac2e048187b4e8a64f3e587c5666 |
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': 5e-05, 'decay_steps': 12795, '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-07, 'amsgrad': False} - training_precision: float32 | 0daa1ad5bf7b3650b15cc1585c1a536f |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.4876 | 0.7620 | 0.5001 | 0.7842 | 0 | | 81fffc2a7ce6af4099fcbd15a1203c4c |
cc-by-4.0 | [] | false | Danish ELECTRA small (cased) An [ELECTRA](https://arxiv.org/abs/2003.10555) model pretrained on a custom Danish corpus (~17.5gb). For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: https://github.com/sarnikowski/danish_transformers/tree/main/electra | e3a88ca74ff2b9ba172d1e74e304dcf4 |
cc-by-4.0 | [] | false | Usage ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sarnikowski/electra-small-generator-da-256-cased") model = AutoModel.from_pretrained("sarnikowski/electra-small-generator-da-256-cased") ``` | 70cd7c5ce6fddb29dd1513e0f6184a24 |
cc-by-4.0 | [] | false | Questions? If you have any questions feel free to open an issue in the [danish_transformers](https://github.com/sarnikowski/danish_transformers) repository, or send an email to p.sarnikowski@gmail.com | 782946cea92d2a45db0f7195a12012ea |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_20k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 1, Step 20k 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 | 87f10ac689a5969def7f3cbf96c7670b |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_20k'] | 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_1-step_20k') model = TFBertModel.from_pretrained("google/multiberts-seed_1-step_20k") 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_1-step_20k') model = BertModel.from_pretrained("google/multiberts-seed_1-step_20k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | ea77224b8ffc30b6032ce662e6a385c5 |
mit | ['generated_from_trainer'] | false | label-transfer This model is a fine-tuned version of [saattrupdan/verdict-classifier](https://huggingface.co/saattrupdan/verdict-classifier) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0452 - F1 Macro: 0.9872 - F1 Misinformation: 0.9918 - F1 Factual: 0.9979 - F1 Other: 0.9720 - Prec Macro: 0.9842 - Prec Misinformation: 0.9958 - Prec Factual: 0.9979 - Prec Other: 0.9588 | d15da288c83e6e010316c9b0156938f8 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 2048 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1423 - num_epochs: 1000 | 1c9063c4d2bedb937217495ef26c0847 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Misinformation | F1 Factual | F1 Other | Prec Macro | Prec Misinformation | Prec Factual | Prec Other | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:----------:|:--------:|:----------:|:-------------------:|:------------:|:----------:| | 0.4236 | 0.9 | 5 | 0.4070 | 0.8866 | 0.9477 | 0.9658 | 0.7463 | 0.9306 | 0.9075 | 0.9766 | 0.9077 | | 0.4175 | 1.9 | 10 | 0.4001 | 0.8872 | 0.9480 | 0.9658 | 0.7477 | 0.9308 | 0.9079 | 0.9766 | 0.9080 | | 0.4115 | 2.9 | 15 | 0.3884 | 0.8896 | 0.9487 | 0.9668 | 0.7534 | 0.9323 | 0.9093 | 0.9787 | 0.9090 | | 0.3932 | 3.9 | 20 | 0.3719 | 0.8943 | 0.9509 | 0.9668 | 0.7652 | 0.9343 | 0.9133 | 0.9787 | 0.9110 | | 0.3785 | 4.9 | 25 | 0.3505 | 0.8973 | 0.9522 | 0.9668 | 0.7730 | 0.9353 | 0.9160 | 0.9787 | 0.9112 | | 0.3653 | 5.9 | 30 | 0.3266 | 0.9009 | 0.9535 | 0.9683 | 0.7809 | 0.9369 | 0.9186 | 0.9818 | 0.9104 | | 0.3337 | 6.9 | 35 | 0.3028 | 0.9143 | 0.9599 | 0.9694 | 0.8137 | 0.9425 | 0.9310 | 0.9818 | 0.9148 | | 0.3181 | 7.9 | 40 | 0.2796 | 0.9181 | 0.9624 | 0.9673 | 0.8245 | 0.9431 | 0.9361 | 0.9807 | 0.9125 | | 0.2976 | 8.9 | 45 | 0.2570 | 0.9199 | 0.9633 | 0.9673 | 0.8291 | 0.9434 | 0.9383 | 0.9807 | 0.9113 | | 0.2845 | 9.9 | 50 | 0.2349 | 0.9242 | 0.9658 | 0.9668 | 0.8401 | 0.9453 | 0.9430 | 0.9797 | 0.9131 | | 0.2649 | 10.9 | 55 | 0.2134 | 0.9270 | 0.9673 | 0.9668 | 0.8470 | 0.9451 | 0.9472 | 0.9797 | 0.9086 | | 0.2399 | 11.9 | 60 | 0.1929 | 0.9330 | 0.9704 | 0.9668 | 0.8619 | 0.9467 | 0.9547 | 0.9797 | 0.9057 | | 0.224 | 12.9 | 65 | 0.1735 | 0.9369 | 0.9724 | 0.9673 | 0.8710 | 0.9467 | 0.9608 | 0.9797 | 0.8996 | | 0.1992 | 13.9 | 70 | 0.1564 | 0.9496 | 0.9783 | 0.9711 | 0.8995 | 0.9531 | 0.9744 | 0.9809 | 0.9039 | | 0.1908 | 14.9 | 75 | 0.1427 | 0.9501 | 0.9784 | 0.9711 | 0.9006 | 0.9519 | 0.9765 | 0.9799 | 0.8993 | | 0.1785 | 15.9 | 80 | 0.1309 | 0.9542 | 0.9790 | 0.9765 | 0.9072 | 0.9549 | 0.9782 | 0.9791 | 0.9076 | | 0.1637 | 16.9 | 85 | 0.1215 | 0.9531 | 0.9791 | 0.9745 | 0.9056 | 0.9536 | 0.9784 | 0.9750 | 0.9073 | | 0.151 | 17.9 | 90 | 0.1131 | 0.9540 | 0.9787 | 0.9771 | 0.9064 | 0.9549 | 0.9776 | 0.9771 | 0.9099 | | 0.1395 | 18.9 | 95 | 0.1049 | 0.9555 | 0.9790 | 0.9787 | 0.9088 | 0.9558 | 0.9784 | 0.9772 | 0.9119 | | 0.1285 | 19.9 | 100 | 0.0963 | 0.9600 | 0.9799 | 0.9833 | 0.9169 | 0.9602 | 0.9798 | 0.9843 | 0.9164 | | 0.1228 | 20.9 | 105 | 0.0887 | 0.9654 | 0.9829 | 0.9844 | 0.9289 | 0.9639 | 0.9850 | 0.9854 | 0.9215 | | 0.1163 | 21.9 | 110 | 0.0832 | 0.9672 | 0.9839 | 0.9849 | 0.9329 | 0.9655 | 0.9864 | 0.9864 | 0.9237 | | 0.1045 | 22.9 | 115 | 0.0792 | 0.9690 | 0.9849 | 0.9849 | 0.9374 | 0.9666 | 0.9883 | 0.9864 | 0.9251 | | 0.0975 | 23.9 | 120 | 0.0758 | 0.9701 | 0.9854 | 0.9854 | 0.9396 | 0.9682 | 0.9880 | 0.9864 | 0.9303 | | 0.0957 | 24.9 | 125 | 0.0731 | 0.9710 | 0.9856 | 0.9864 | 0.9411 | 0.9691 | 0.9883 | 0.9885 | 0.9305 | | 0.0911 | 25.9 | 130 | 0.0702 | 0.9743 | 0.9862 | 0.9901 | 0.9467 | 0.9722 | 0.9891 | 0.9896 | 0.9377 | | 0.0884 | 26.9 | 135 | 0.0676 | 0.9759 | 0.9875 | 0.9901 | 0.9502 | 0.9728 | 0.9916 | 0.9886 | 0.9381 | | 0.087 | 27.9 | 140 | 0.0652 | 0.9770 | 0.9878 | 0.9912 | 0.9521 | 0.9739 | 0.9919 | 0.9906 | 0.9392 | | 0.0813 | 28.9 | 145 | 0.0631 | 0.9791 | 0.9880 | 0.9938 | 0.9555 | 0.9758 | 0.9925 | 0.9938 | 0.9412 | | 0.0758 | 29.9 | 150 | 0.0612 | 0.9805 | 0.9887 | 0.9943 | 0.9584 | 0.9767 | 0.9938 | 0.9938 | 0.9424 | | 0.0734 | 30.9 | 155 | 0.0598 | 0.9796 | 0.9882 | 0.9943 | 0.9564 | 0.9762 | 0.9927 | 0.9938 | 0.9422 | | 0.0713 | 31.9 | 160 | 0.0586 | 0.9798 | 0.9883 | 0.9943 | 0.9569 | 0.9765 | 0.9927 | 0.9938 | 0.9430 | | 0.0662 | 32.9 | 165 | 0.0568 | 0.9805 | 0.9887 | 0.9943 | 0.9584 | 0.9768 | 0.9936 | 0.9938 | 0.9432 | | 0.063 | 33.9 | 170 | 0.0552 | 0.9813 | 0.9893 | 0.9943 | 0.9602 | 0.9778 | 0.9938 | 0.9938 | 0.9459 | | 0.0623 | 34.9 | 175 | 0.0538 | 0.9819 | 0.9897 | 0.9943 | 0.9616 | 0.9785 | 0.9941 | 0.9938 | 0.9477 | | 0.0601 | 35.9 | 180 | 0.0531 | 0.9828 | 0.9901 | 0.9948 | 0.9635 | 0.9793 | 0.9947 | 0.9938 | 0.9496 | | 0.0549 | 36.9 | 185 | 0.0521 | 0.9826 | 0.9900 | 0.9948 | 0.9631 | 0.9790 | 0.9947 | 0.9938 | 0.9487 | | 0.0539 | 37.9 | 190 | 0.0512 | 0.9824 | 0.9898 | 0.9948 | 0.9626 | 0.9789 | 0.9944 | 0.9938 | 0.9486 | | 0.0525 | 38.9 | 195 | 0.0503 | 0.9827 | 0.9898 | 0.9953 | 0.9630 | 0.9792 | 0.9944 | 0.9938 | 0.9495 | | 0.0494 | 39.9 | 200 | 0.0498 | 0.9831 | 0.9898 | 0.9958 | 0.9635 | 0.9796 | 0.9944 | 0.9948 | 0.9496 | | 0.0502 | 40.9 | 205 | 0.0489 | 0.9838 | 0.9901 | 0.9964 | 0.9650 | 0.9804 | 0.9947 | 0.9958 | 0.9506 | | 0.0499 | 41.9 | 210 | 0.0483 | 0.9845 | 0.9904 | 0.9969 | 0.9663 | 0.9813 | 0.9947 | 0.9958 | 0.9532 | | 0.0484 | 42.9 | 215 | 0.0480 | 0.9847 | 0.9905 | 0.9969 | 0.9668 | 0.9814 | 0.9950 | 0.9958 | 0.9533 | | 0.0465 | 43.9 | 220 | 0.0477 | 0.9852 | 0.9908 | 0.9969 | 0.9678 | 0.9816 | 0.9955 | 0.9958 | 0.9534 | | 0.0453 | 44.9 | 225 | 0.0474 | 0.9856 | 0.9911 | 0.9969 | 0.9687 | 0.9822 | 0.9955 | 0.9958 | 0.9551 | | 0.0452 | 45.9 | 230 | 0.0471 | 0.9856 | 0.9911 | 0.9969 | 0.9687 | 0.9822 | 0.9955 | 0.9958 | 0.9551 | | 0.0453 | 46.9 | 235 | 0.0469 | 0.9854 | 0.9910 | 0.9969 | 0.9682 | 0.9821 | 0.9953 | 0.9958 | 0.9551 | | 0.043 | 47.9 | 240 | 0.0468 | 0.9858 | 0.9912 | 0.9969 | 0.9692 | 0.9825 | 0.9955 | 0.9958 | 0.9560 | | 0.0428 | 48.9 | 245 | 0.0465 | 0.9856 | 0.9911 | 0.9969 | 0.9687 | 0.9824 | 0.9953 | 0.9958 | 0.9560 | | 0.0414 | 49.9 | 250 | 0.0465 | 0.9852 | 0.9911 | 0.9964 | 0.9682 | 0.9820 | 0.9953 | 0.9948 | 0.9560 | | 0.0388 | 50.9 | 255 | 0.0462 | 0.9852 | 0.9911 | 0.9964 | 0.9682 | 0.9820 | 0.9953 | 0.9948 | 0.9560 | | 0.0404 | 51.9 | 260 | 0.0458 | 0.9852 | 0.9911 | 0.9964 | 0.9682 | 0.9820 | 0.9953 | 0.9948 | 0.9560 | | 0.0382 | 52.9 | 265 | 0.0454 | 0.9856 | 0.9911 | 0.9969 | 0.9687 | 0.9824 | 0.9953 | 0.9958 | 0.9560 | | 0.042 | 53.9 | 270 | 0.0443 | 0.9862 | 0.9911 | 0.9979 | 0.9697 | 0.9831 | 0.9953 | 0.9979 | 0.9561 | | 0.0369 | 54.9 | 275 | 0.0438 | 0.9862 | 0.9911 | 0.9979 | 0.9697 | 0.9831 | 0.9953 | 0.9979 | 0.9561 | | 0.0383 | 55.9 | 280 | 0.0437 | 0.9862 | 0.9911 | 0.9979 | 0.9697 | 0.9831 | 0.9953 | 0.9979 | 0.9561 | | 0.0373 | 56.9 | 285 | 0.0438 | 0.9862 | 0.9911 | 0.9979 | 0.9696 | 0.9833 | 0.9950 | 0.9979 | 0.9569 | | 0.0402 | 57.9 | 290 | 0.0440 | 0.9862 | 0.9911 | 0.9979 | 0.9696 | 0.9833 | 0.9950 | 0.9979 | 0.9569 | | 0.0389 | 58.9 | 295 | 0.0443 | 0.9858 | 0.9908 | 0.9979 | 0.9687 | 0.9831 | 0.9944 | 0.9979 | 0.9568 | | 0.0361 | 59.9 | 300 | 0.0443 | 0.9860 | 0.9910 | 0.9979 | 0.9692 | 0.9832 | 0.9947 | 0.9979 | 0.9569 | | 0.0369 | 60.9 | 305 | 0.0442 | 0.9860 | 0.9910 | 0.9979 | 0.9692 | 0.9832 | 0.9947 | 0.9979 | 0.9569 | | 0.0353 | 61.9 | 310 | 0.0442 | 0.9862 | 0.9911 | 0.9979 | 0.9696 | 0.9833 | 0.9950 | 0.9979 | 0.9569 | | 0.035 | 62.9 | 315 | 0.0446 | 0.9860 | 0.9910 | 0.9979 | 0.9692 | 0.9832 | 0.9947 | 0.9979 | 0.9569 | | 0.0352 | 63.9 | 320 | 0.0449 | 0.9864 | 0.9912 | 0.9979 | 0.9701 | 0.9834 | 0.9953 | 0.9979 | 0.9570 | | 0.0336 | 64.9 | 325 | 0.0451 | 0.9860 | 0.9910 | 0.9979 | 0.9692 | 0.9832 | 0.9947 | 0.9979 | 0.9569 | | 0.0317 | 65.9 | 330 | 0.0448 | 0.9860 | 0.9910 | 0.9979 | 0.9692 | 0.9832 | 0.9947 | 0.9979 | 0.9569 | | 0.0334 | 66.9 | 335 | 0.0447 | 0.9866 | 0.9914 | 0.9979 | 0.9705 | 0.9843 | 0.9944 | 0.9979 | 0.9605 | | 0.0316 | 67.9 | 340 | 0.0447 | 0.9860 | 0.9910 | 0.9979 | 0.9691 | 0.9834 | 0.9944 | 0.9979 | 0.9577 | | 0.0329 | 68.9 | 345 | 0.0451 | 0.9866 | 0.9914 | 0.9979 | 0.9706 | 0.9835 | 0.9955 | 0.9979 | 0.9570 | | 0.0326 | 69.9 | 350 | 0.0454 | 0.9866 | 0.9914 | 0.9979 | 0.9706 | 0.9835 | 0.9955 | 0.9979 | 0.9570 | | 0.032 | 70.9 | 355 | 0.0453 | 0.9868 | 0.9915 | 0.9979 | 0.9711 | 0.9838 | 0.9955 | 0.9979 | 0.9579 | | 0.0325 | 71.9 | 360 | 0.0450 | 0.9864 | 0.9912 | 0.9979 | 0.9701 | 0.9836 | 0.9950 | 0.9979 | 0.9578 | | 0.0319 | 72.9 | 365 | 0.0446 | 0.9868 | 0.9915 | 0.9979 | 0.9711 | 0.9838 | 0.9955 | 0.9979 | 0.9579 | | 0.0326 | 73.9 | 370 | 0.0444 | 0.9868 | 0.9915 | 0.9979 | 0.9711 | 0.9838 | 0.9955 | 0.9979 | 0.9579 | | 0.0315 | 74.9 | 375 | 0.0442 | 0.9873 | 0.9918 | 0.9979 | 0.9721 | 0.9840 | 0.9961 | 0.9979 | 0.9580 | | 0.0304 | 75.9 | 380 | 0.0442 | 0.9866 | 0.9914 | 0.9979 | 0.9706 | 0.9837 | 0.9953 | 0.9979 | 0.9579 | | 0.03 | 76.9 | 385 | 0.0444 | 0.9864 | 0.9912 | 0.9979 | 0.9702 | 0.9832 | 0.9955 | 0.9979 | 0.9561 | | 0.0296 | 77.9 | 390 | 0.0448 | 0.9862 | 0.9911 | 0.9979 | 0.9697 | 0.9831 | 0.9953 | 0.9979 | 0.9561 | | 0.0307 | 78.9 | 395 | 0.0452 | 0.9866 | 0.9914 | 0.9979 | 0.9706 | 0.9837 | 0.9953 | 0.9979 | 0.9579 | | 0.0296 | 79.9 | 400 | 0.0453 | 0.9862 | 0.9911 | 0.9979 | 0.9697 | 0.9831 | 0.9953 | 0.9979 | 0.9561 | | 0.0292 | 80.9 | 405 | 0.0454 | 0.9862 | 0.9911 | 0.9979 | 0.9697 | 0.9831 | 0.9953 | 0.9979 | 0.9561 | | 0.0293 | 81.9 | 410 | 0.0452 | 0.9862 | 0.9911 | 0.9979 | 0.9697 | 0.9829 | 0.9955 | 0.9979 | 0.9552 | | 0.0292 | 82.9 | 415 | 0.0454 | 0.9862 | 0.9911 | 0.9979 | 0.9697 | 0.9829 | 0.9955 | 0.9979 | 0.9552 | | 0.0281 | 83.9 | 420 | 0.0454 | 0.9866 | 0.9914 | 0.9979 | 0.9706 | 0.9833 | 0.9958 | 0.9979 | 0.9562 | | 0.0298 | 84.9 | 425 | 0.0452 | 0.9872 | 0.9918 | 0.9979 | 0.9720 | 0.9842 | 0.9958 | 0.9979 | 0.9588 | | f5f2228c810d76f43b36a8278655b358 |
apache-2.0 | ['translation'] | false | opus-mt-ase-es * source languages: ase * target languages: es * OPUS readme: [ase-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ase-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/ase-es/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ase-es/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ase-es/opus-2020-01-20.eval.txt) | ffa5b41cc43fd698dd5abce3ede5ca85 |
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: 0.0590 - Precision: 0.9357 - Recall: 0.9507 - F1: 0.9432 - Accuracy: 0.9867 | 962698a7fa0f01387d844d23a708f8ca |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0872 | 1.0 | 1756 | 0.0709 | 0.9194 | 0.9334 | 0.9263 | 0.9822 | | 0.033 | 2.0 | 3512 | 0.0622 | 0.9298 | 0.9497 | 0.9396 | 0.9861 | | 0.0183 | 3.0 | 5268 | 0.0590 | 0.9357 | 0.9507 | 0.9432 | 0.9867 | | 10680e0fdcc24bc9ededd2e459382676 |
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