license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-ner-conll2003 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: 0.0602 - Precision: 0.9342 - Recall: 0.9536 - F1: 0.9438 - Accuracy: 0.9870 | b9a7ea899da4da463c8f5e4c784f73ae |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0871 | 1.0 | 1756 | 0.0728 | 0.9138 | 0.9275 | 0.9206 | 0.9811 | | 0.0331 | 2.0 | 3512 | 0.0591 | 0.9311 | 0.9514 | 0.9411 | 0.9866 | | 0.0173 | 3.0 | 5268 | 0.0602 | 0.9342 | 0.9536 | 0.9438 | 0.9870 | | 5f73bd5f3c7b0cf8348d31f8b29fd19e |
gpl-3.0 | ['object-detection', 'computer-vision', 'yolor', 'yolov4'] | false | Yolov6 Inference ```python from yolor.helpers import Yolor model = Yolor( cfg='yolor/cfg/yolor_w6.cfg', weights='kadirnar/yolor-w6', imgsz=640, device='cuda:0' ) model.classes = None model.conf = 0.25 model.iou_ = 0.45 model.show = False model.save = True model.predict('yolor/data/highway.jpg') ``` | 871206a75e9ed1331ed1c577927d9b0b |
creativeml-openrail-m | ['text-to-image'] | false | Asoon Dreambooth SD Model Dreambooth model trained by AlekseyCalvin with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-768 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: To generate custom images of my primary public self – one known as A.C.T. SOON® – use "asoon" or "asoon person" in your Stable Diffusion prompt (implemented via this model only). Checkpoints herein trained based on SD 2.1. [asoon:](https://huggingface.co/AlekseyCalvin/asoon-dreambooth-sd-model/resolve/main/concept_images/asoon_%2812%29.jpg)! | d99cf76e6d60c7cd16b8d3bb2f2daaab |
mit | ['generated_from_trainer'] | false | bart-large-cnn-summarizer_03 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0999 - Rouge1: 51.6222 - Rouge2: 33.428 - Rougel: 40.2093 - Rougelsum: 47.7154 - Gen Len: 102.7962 | d188a32e3f81594bf0596267e9f90041 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 0.9348 | 1.0 | 17166 | 0.9969 | 51.0763 | 32.9497 | 39.6851 | 47.0744 | 99.664 | | 0.7335 | 2.0 | 34332 | 1.0019 | 51.8002 | 33.8081 | 40.5887 | 47.9445 | 99.7884 | | 0.471 | 3.0 | 51498 | 1.0999 | 51.6222 | 33.428 | 40.2093 | 47.7154 | 102.7962 | | 2b559121004873f373884316cc4a1965 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'question-generation', 'squad_it', 'text2text-generation'] | false | mT5 Small for Question Generation 💭 🇮🇹 This repository contains the checkpoint for the [mT5 Small](https://huggingface.co/google/mt5-small) model fine-tuned on question generation on the [SQuAD-IT corpus](https://huggingface.co/datasets/squad_it) as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io). A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach. | 48b415caa4dc03f55ab7a2c42cc1dc7f |
apache-2.0 | ['italian', 'sequence-to-sequence', 'question-generation', 'squad_it', 'text2text-generation'] | false | Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines qg = pipeline("text2text-generation", model='it5/mt5-small-question-generation') qg("Le conoscenze mediche erano stagnanti durante il Medioevo. Il resoconto più autorevole di allora è venuto dalla facoltà di medicina di Parigi in un rapporto al re di Francia che ha incolpato i cieli, sotto forma di una congiunzione di tre pianeti nel 1345 che causò una "grande pestilenza nell\' aria". Questa relazione è diventata la prima e più diffusa di una serie di casi di peste che cercava di dare consigli ai malati. Che la peste fosse causata dalla cattiva aria divenne la teoria più accettata. Oggi, questo è conosciuto come la teoria di Miasma. La parola "peste" non aveva un significato particolare in questo momento, e solo la ricorrenza dei focolai durante il Medioevo gli diede il nome che è diventato il termine medico. Risposta: re di Francia") >>> [{"generated_text": "Per chi è stato redatto il referto medico?"}] ``` or loaded using autoclasses: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("it5/mt5-small-question-generation") model = AutoModelForSeq2SeqLM.from_pretrained("it5/mt5-small-question-generation") ``` If you use this model in your research, please cite our work as: ```bibtex @article{sarti-nissim-2022-it5, title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation}, author={Sarti, Gabriele and Nissim, Malvina}, journal={ArXiv preprint 2203.03759}, url={https://arxiv.org/abs/2203.03759}, year={2022}, month={mar} } ``` | 25702271b8929ce86144b6d0b36c69f1 |
apache-2.0 | ['generated_from_keras_callback'] | false | krm/mt5-small-OrangeSum-Summarizer 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: 3.7771 - Validation Loss: 2.5727 - Epoch: 7 | 239f9e73fa47ec1bbffc48e93eb67f9f |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 5000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 | 49ea8e38831d71ca58ef3931c03fa040 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 7.8346 | 3.2199 | 0 | | 5.1020 | 2.8619 | 1 | | 4.5632 | 2.7564 | 2 | | 4.2564 | 2.6726 | 3 | | 4.0501 | 2.6300 | 4 | | 3.9185 | 2.5930 | 5 | | 3.8209 | 2.5792 | 6 | | 3.7771 | 2.5727 | 7 | | 667b8e4e935a701ef04078719faac836 |
apache-2.0 | ['t5', 'seq2seq'] | false | t5-eff-large-8l-dutch-english-cased A [T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) sequence to sequence model pre-trained from scratch on [cleaned Dutch 🇳🇱🇧🇪 mC4 and cleaned English 🇬🇧 C4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned). This **t5 eff** model has **334M** parameters. It was pre-trained with masked language modeling (denoise token span corruption) objective on the dataset `mc4_nl_cleaned` config `large_en_nl` for **1** epoch(s) and a duration of **3d 23h**, with a sequence length of **512**, batch size **128** and **851850** total steps (**56B** tokens). Pre-training evaluation loss and accuracy are **1,15** and **0,74**. Refer to the evaluation section below for a comparison of the pre-trained models on summarization and translation. * Pre-trained T5 models need to be finetuned before they can be used for downstream tasks, therefore the inference widget on the right has been turned off. * For a demo of the Dutch CNN summarization models, head over to the Hugging Face Spaces for the **[Netherformer 📰](https://huggingface.co/spaces/flax-community/netherformer)** example application! Please refer to the original T5 papers and Scale Efficiently papers for more information about the T5 architecture and configs, though it must be noted that this model (t5-eff-large-8l-dutch-english-cased) is unrelated to these projects and not an 'official' checkpoint. * **[Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf)** by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*. * **[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*. | b369f6748d462d366f2a2fc9f3380e2d |
mit | ['generated_from_trainer'] | false | luganda-ner-v1 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the lg-ner dataset. It achieves the following results on the evaluation set: - Loss: 1.0530 - Precision: 0.2902 - Recall: 0.2772 - F1: 0.2835 - Accuracy: 0.7298 | aa5ddea1c32649c3d4e52eadd63f5ea5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 25 | 1.2878 | 0.0 | 0.0 | 0.0 | 0.7271 | | No log | 2.0 | 50 | 1.2373 | 0.0 | 0.0 | 0.0 | 0.7271 | | No log | 3.0 | 75 | 1.2309 | 0.3542 | 0.1683 | 0.2282 | 0.7244 | | No log | 4.0 | 100 | 1.1505 | 0.2712 | 0.2376 | 0.2533 | 0.7183 | | No log | 5.0 | 125 | 1.1360 | 0.2579 | 0.2426 | 0.25 | 0.7170 | | No log | 6.0 | 150 | 1.0932 | 0.3108 | 0.2277 | 0.2629 | 0.7338 | | No log | 7.0 | 175 | 1.0761 | 0.2989 | 0.2574 | 0.2766 | 0.7298 | | No log | 8.0 | 200 | 1.0645 | 0.2805 | 0.3069 | 0.2931 | 0.7244 | | No log | 9.0 | 225 | 1.0577 | 0.3022 | 0.2723 | 0.2865 | 0.7325 | | No log | 10.0 | 250 | 1.0530 | 0.2902 | 0.2772 | 0.2835 | 0.7298 | | 3cd16e5a0a8883c1c8c3b0ed6bd27eab |
apache-2.0 | [] | false | 8209;lm|✔|[🤗](https://huggingface.co/north/t5_base_NCC_lm)|[🤗](https://huggingface.co/north/t5_large_NCC_lm)|[🤗](https://huggingface.co/north/t5_xl_NCC_lm)|[🤗](https://huggingface.co/north/t5_xxl_NCC_lm)|| | f833e58a4ece4b4660377b7017d9a7d1 |
apache-2.0 | [] | false | DistilBERT base cased distilled SQuAD > Note: This model is a clone of [`distilbert-base-cased-distilled-squad`](https://huggingface.co/distilbert-base-cased-distilled-squad) for internal testing. This model is a fine-tune checkpoint of [DistilBERT-base-cased](https://huggingface.co/distilbert-base-cased), fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1. This model reaches a F1 score of 87.1 on the dev set (for comparison, BERT bert-base-cased version reaches a F1 score of 88.7). Using the question answering `Evaluator` from evaluate gives: ``` {'exact_match': 79.54588457899716, 'f1': 86.81181300991533, 'latency_in_seconds': 0.008683730778997168, 'samples_per_second': 115.15787689073015, 'total_time_in_seconds': 91.78703433400005} ``` which is roughly consistent with the official score. | 437d2399ceddc2bc2bfc3b35c65b5ff7 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-Pisa This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1132 | dd3cdd1147ccfb6281c808cbeb5a62a3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 9 | 1.4146 | | No log | 2.0 | 18 | 1.1013 | | No log | 3.0 | 27 | 1.1237 | | e8055a4f96ec70eb3f2e3e1463caa19c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | false | Test Wav2Vec2 with egyptian arabic Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Egyptian using the [arabicspeech.org MGB-3](https://arabicspeech.org/mgb3-asr/) When using this model, make sure that your speech input is sampled at 16kHz. | 198c2bc596082a733b22570b42c55ebc |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech'] | 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 dataset = load_dataset("arabic_speech_corpus", split="test") processor = Wav2Vec2Processor.from_pretrained("othrif/wav2vec_test") model = Wav2Vec2ForCTC.from_pretrained("othrif/wav2vec_test") resampler = torchaudio.transforms.Resample(48_000, 16_000) | 6dc02699b0217939534e60fee980d64c |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 0cabe65afd362122e77b04e2e967986a91de0fd8 pip install -e . cd egs2/callhome/diar1 ./run.sh --skip_data_prep false --skip_train true --download_model YushiUeda/callhome_adapt_real ``` <!-- Generated by scripts/utils/show_diar_result.sh --> | 1b7dfc98b931497b29a682a584dca55a |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | Environments - date: `Mon Jun 20 10:30:23 EDT 2022` - python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]` - espnet version: `espnet 202205` - pytorch version: `pytorch 1.9.1+cu102` - Git hash: `fc62b1ce3e50c5ef8a2ac8cedb0d92ac41df54ca` - Commit date: `Thu Jun 9 16:29:52 2022 +0900` | d357b0f817ef699cf9203669fd5d1f1b |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | DER diarized_callhome2_spkall |threshold_median_collar|DER| |---|---| |result_th0.3_med11_collar0.25|22.29| |result_th0.3_med1_collar0.25|23.27| |result_th0.4_med11_collar0.25|19.85| |result_th0.4_med1_collar0.25|20.80| |result_th0.5_med11_collar0.25|19.26| |result_th0.5_med1_collar0.25|20.18| |result_th0.6_med11_collar0.25|20.24| |result_th0.6_med1_collar0.25|21.08| |result_th0.7_med11_collar0.25|22.38| |result_th0.7_med1_collar0.25|23.17| | 4ca889f324e765a1cead250346a0d0e4 |
cc-by-4.0 | ['espnet', 'audio', 'diarization'] | false | DIAR config <details><summary>expand</summary> ``` config: conf/tuning/train_diar_eda_adapt.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/diar_train_diar_eda_adapt_real_lr0001 ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max - - train - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5 grad_clip_type: 2.0 grad_noise: false accum_grad: 16 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: - exp/diar_train_diar_eda_adapt_simu/latest.pth ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 1 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: sorted valid_batch_type: null fold_length: - 80000 - 800 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/callhome1_spkall/wav.scp - speech - sound - - dump/raw/callhome1_spkall/espnet_rttm - spk_labels - rttm valid_data_path_and_name_and_type: - - dump/raw/callhome2_spkall/wav.scp - speech - sound - - dump/raw/callhome2_spkall/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: 0.001 scheduler: null scheduler_conf: {} num_spk: 7 init: null input_size: null model_conf: attractor_weight: 1.0 use_preprocessor: true frontend: default frontend_conf: fs: 8k hop_length: 128 specaug: specaug specaug_conf: apply_time_warp: false apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/diar_stats_8k/train/feats_stats.npz encoder: transformer encoder_conf: input_layer: conv2d num_blocks: 4 linear_units: 512 dropout_rate: 0.1 output_size: 256 attention_heads: 4 attention_dropout_rate: 0.1 decoder: linear decoder_conf: {} label_aggregator: label_aggregator label_aggregator_conf: win_length: 1024 hop_length: 512 attractor: rnn attractor_conf: unit: 256 layer: 1 dropout: 0.0 attractor_grad: false required: - output_dir version: '202204' distributed: false ``` </details> | de4c5f56de80aee29cef8bd194724ce5 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.1004 - Accuracy: 0.9432 | b1db864a38c6c7185d020d5e969af54e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9044 | 1.0 | 318 | 0.5748 | 0.7390 | | 0.4491 | 2.0 | 636 | 0.2876 | 0.88 | | 0.2538 | 3.0 | 954 | 0.1813 | 0.9229 | | 0.1765 | 4.0 | 1272 | 0.1388 | 0.9294 | | 0.1422 | 5.0 | 1590 | 0.1214 | 0.9345 | | 0.1243 | 6.0 | 1908 | 0.1114 | 0.9406 | | 0.1138 | 7.0 | 2226 | 0.1066 | 0.94 | | 0.1076 | 8.0 | 2544 | 0.1030 | 0.9423 | | 0.104 | 9.0 | 2862 | 0.1010 | 0.9419 | | 0.1019 | 10.0 | 3180 | 0.1004 | 0.9432 | | 7bbb5137879a1e0baa1f9253e2070710 |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'roberta', 'pytorch'] | false | Model description  Digital humanities research needs the support of large-scale corpus and high-performance ancient Chinese natural language processing tools. The pre-training language model has greatly improved the accuracy of text mining in English and modern Chinese texts. At present, there is an urgent need for a pre-training model specifically for the automatic processing of ancient texts. We used the verified high-quality “Siku Quanshu” full-text corpus as the training set, based on the BERT deep language model architecture, we constructed the SikuBERT and SikuRoBERTa pre-training language models for intelligent processing tasks of ancient Chinese. | 78093a1cfa4f3c20c2a0fa7de67e7031 |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'roberta', 'pytorch'] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SIKU-BERT/sikuroberta") model = AutoModel.from_pretrained("SIKU-BERT/sikuroberta") ``` | 19d159242c75d7d25697bf34db215e7f |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'roberta', 'pytorch'] | false | About Us We are from Nanjing Agricultural University. > Created with by SIKU-BERT [](https://github.com/SIKU-BERT/SikuBERT-for-digital-humanities-and-classical-Chinese-information-processing) | c99f90a7a7dbef462a88eb419e66e445 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-misogyny-sexism-out-of-sample-test-opt-bal This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2811 - Accuracy: 0.6022 - F1: 0.5689 - Precision: 0.5624 - Recall: 0.5756 - Mae: 0.3978 | ae230e58775b4e23908282fddbd29071 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:| | 0.4434 | 1.0 | 1100 | 0.8792 | 0.5752 | 0.4897 | 0.5414 | 0.4469 | 0.4248 | | 0.3592 | 2.0 | 2200 | 1.0511 | 0.5882 | 0.4597 | 0.5723 | 0.3841 | 0.4118 | | 0.3351 | 3.0 | 3300 | 0.8862 | 0.5639 | 0.5437 | 0.5199 | 0.5698 | 0.4361 | | 0.2649 | 4.0 | 4400 | 1.5065 | 0.5931 | 0.5467 | 0.5556 | 0.5381 | 0.4069 | | 0.2252 | 5.0 | 5500 | 1.2637 | 0.5766 | 0.6084 | 0.5261 | 0.7212 | 0.4234 | | 0.2234 | 6.0 | 6600 | 1.6854 | 0.5832 | 0.5419 | 0.5432 | 0.5405 | 0.4168 | | 0.2288 | 7.0 | 7700 | 1.7353 | 0.5985 | 0.5917 | 0.5517 | 0.6380 | 0.4015 | | 0.2008 | 8.0 | 8800 | 1.8444 | 0.6152 | 0.5693 | 0.5814 | 0.5577 | 0.3848 | | 0.1765 | 9.0 | 9900 | 2.4235 | 0.5833 | 0.5508 | 0.5417 | 0.5601 | 0.4167 | | 0.2334 | 10.0 | 11000 | 2.0034 | 0.6002 | 0.5635 | 0.5611 | 0.5659 | 0.3998 | | 0.1561 | 11.0 | 12100 | 2.3651 | 0.5897 | 0.5772 | 0.5445 | 0.6142 | 0.4103 | | 0.1759 | 12.0 | 13200 | 2.8745 | 0.5855 | 0.5742 | 0.5402 | 0.6128 | 0.4145 | | 0.1306 | 13.0 | 14300 | 2.7506 | 0.5904 | 0.5830 | 0.5442 | 0.6278 | 0.4096 | | 0.1443 | 14.0 | 15400 | 2.7292 | 0.6061 | 0.5549 | 0.5725 | 0.5383 | 0.3939 | | 0.1124 | 15.0 | 16500 | 2.6693 | 0.6119 | 0.5744 | 0.5745 | 0.5742 | 0.3881 | | 0.0886 | 16.0 | 17600 | 2.8332 | 0.6052 | 0.5708 | 0.5661 | 0.5756 | 0.3948 | | 0.078 | 17.0 | 18700 | 3.0623 | 0.6054 | 0.5693 | 0.5668 | 0.5718 | 0.3946 | | 0.0955 | 18.0 | 19800 | 3.1543 | 0.5965 | 0.5725 | 0.5538 | 0.5925 | 0.4035 | | 0.0689 | 19.0 | 20900 | 3.3443 | 0.5971 | 0.5763 | 0.5537 | 0.6009 | 0.4029 | | 0.0669 | 20.0 | 22000 | 3.2811 | 0.6022 | 0.5689 | 0.5624 | 0.5756 | 0.3978 | | 3199c428ae0c3e517478e3cfd0118400 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-skin-cancer This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. | e7e54218cd1a763318b11f1d449ac002 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/libritts_xvector_vits` ♻️ Imported from https://zenodo.org/record/5521416/ This model was trained by kan-bayashi using libritts/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 748f8e12c437aa5e3e8e21330cba7f9a |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Tron Legacy Diffusion This is a fine-tuned Stable Diffusion model (based on v1.5) trained on screenshots from the film **_Tron: Legacy (2010)_**. Use the token **_trnlgcy_** in your prompts to use the style. _Download the ckpt file from "files and versions" tab into the stable diffusion models folder of your web-ui of choice._ -- **Characters rendered with this model:**  _prompt and settings used: **[person] in the style of trnlgcy** | **Steps: 25, Sampler: Euler a, CFG scale: 7.5**_ -- **Landscapes/scenes rendered with this model:**  _prompt and settings used: **city landscape in the style of trnlgcy** | **Steps: 25, Sampler: Euler a, CFG scale: 7.5**_ -- This model was trained with Dreambooth training by TheLastBen, using 30 images at 3000 steps. -- | 76e9f410325ae4e19579a14fa4d5a3a0 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | License This model is 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 model 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 model 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) -- [](https://www.patreon.com/dallinmackay) | 68f0ebdd4946ae7f44fbf3cdff7bf28f |
cc-by-4.0 | ['generated_from_trainer'] | false | danish-roberta-botxo-danish-finetuned-hatespeech This model is for a university project and is uploaded for sharing between students. It is training on a danish hate speech labeled training set. Feel free to use it, but as of now, we don't promise any good results ;-) This model is a fine-tuned version of [flax-community/roberta-base-danish](https://huggingface.co/flax-community/roberta-base-danish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2849 | e2c81784ebc229488b13d9ad0d196442 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 315 | 0.3074 | | 0.3016 | 2.0 | 630 | 0.3152 | | 0.3016 | 3.0 | 945 | 0.2849 | | c396df6f263342d4064f8c8295a8fcbe |
cc-by-4.0 | ['japanese', 'ainu'] | false | Examples | input | output| |---|---| |こんにちは|イランカラプテ| |ありがとうございます|イヤイライケレ| |熊は神ですか|キムンカムイアナクカムイネヤ？| |熊は怖いのか|キムンカムイアナクアシトマプネヤ？| |フクロウは鳥です|イソサンケカムイアナクチカプネ| |分かりません！|ケラムシカレ!| |勉強した？|ヤイホノッカエキプネヤ?| |してないです|クキカソモキ| |さようなら|アプンノオカヤン| | 4647ab20a12438ce6a98cd0abf90c5d6 |
cc-by-4.0 | ['japanese', 'ainu'] | false | License Shield: [![CC BY 4.0][cc-by-shield]][cc-by] This work is licensed under a [Creative Commons Attribution 4.0 International License][cc-by]. [![CC BY 4.0][cc-by-image]][cc-by] [cc-by]: http://creativecommons.org/licenses/by/4.0/ [cc-by-image]: https://i.creativecommons.org/l/by/4.0/88x31.png [cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg | 66754c3934c455640aa7047382c6023e |
apache-2.0 | ['generated_from_trainer'] | false | results This model is a fine-tuned version of [hfl/chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5193 - F1: 0.9546 | 65ed472067707d0b2f086875888712a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 85bdeaaa14b667416064bf1194d655cb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3803 | 1.0 | 1792 | 0.5110 | 0.9546 | | 0.4129 | 2.0 | 3584 | 0.5256 | 0.9546 | | 0.4804 | 3.0 | 5376 | 0.5305 | 0.9546 | | 0.6571 | 4.0 | 7168 | 0.5583 | 0.9546 | | 0.6605 | 5.0 | 8960 | 0.5193 | 0.9546 | | efad2202197a5594c891cfaceeca9d49 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'speech-emotion-recognition'] | false | Prediction ```python import torch import torch.nn as nn import torch.nn.functional as F import torchaudio from transformers import AutoConfig, Wav2Vec2FeatureExtractor import librosa import IPython.display as ipd import numpy as np import pandas as pd ``` ```python device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model_name_or_path = "m3hrdadfi/wav2vec2-xlsr-greek-speech-emotion-recognition" config = AutoConfig.from_pretrained(model_name_or_path) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path) sampling_rate = feature_extractor.sampling_rate model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device) ``` ```python def speech_file_to_array_fn(path, sampling_rate): speech_array, _sampling_rate = torchaudio.load(path) resampler = torchaudio.transforms.Resample(_sampling_rate) speech = resampler(speech_array).squeeze().numpy() return speech def predict(path, sampling_rate): speech = speech_file_to_array_fn(path, sampling_rate) inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True) inputs = {key: inputs[key].to(device) for key in inputs} with torch.no_grad(): logits = model(**inputs).logits scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0] outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)] return outputs ``` ```python path = "/path/to/disgust.wav" outputs = predict(path, sampling_rate) ``` ```bash [ {'Emotion': 'anger', 'Score': '0.0%'}, {'Emotion': 'disgust', 'Score': '99.2%'}, {'Emotion': 'fear', 'Score': '0.1%'}, {'Emotion': 'happiness', 'Score': '0.3%'}, {'Emotion': 'sadness', 'Score': '0.5%'} ] ``` | ff4972cc084944807f59d72ee5943e76 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'speech-emotion-recognition'] | false | Evaluation The following tables summarize the scores obtained by model overall and per each class. | Emotions | precision | recall | f1-score | accuracy | |-----------|-----------|--------|----------|----------| | anger | 0.92 | 1.00 | 0.96 | | | disgust | 0.85 | 0.96 | 0.90 | | | fear | 0.88 | 0.88 | 0.88 | | | happiness | 0.94 | 0.71 | 0.81 | | | sadness | 0.96 | 1.00 | 0.98 | | | | | | Overal | 0.91 | | 5bca26535b1fc8befbe5d312348c5994 |
apache-2.0 | ['distilbert', 'needmining'] | false | Finetuned-Distilbert-needmining (uncased) This model is a finetuned version of the [Distilbert base model](https://huggingface.co/distilbert-base-uncased). It was trained to predict need-containing sentences from amazon product reviews. | 351c7c7d08f6faffbbb04ea4f35954d1 |
apache-2.0 | ['distilbert', 'needmining'] | false | Intended uses & limitations You can use this model to identify sentences that contain customer needs in user-generated content. This can act as a filtering process to remove uninformative content for market research. | 09474094df99c4b4026262a23f0debbb |
apache-2.0 | ['distilbert', 'needmining'] | false | How to use You can use this model directly with a pipeline for text classification: ```python >>> from transformers import pipeline >>> classifier = pipeline("text-classification", model="svenstahlmann/finetuned-distilbert-needmining") >>> classifier("the plasic feels super cheap.") [{'label': 'contains need', 'score': 0.9397542476654053}] ``` | a8f261c9ff252440f7e8ccc4edff9515 |
apache-2.0 | ['distilbert', 'needmining'] | false | Training procedure For the training, we used [Population Based Training (PBT)](https://www.deepmind.com/blog/population-based-training-of-neural-networks) and optimized for f1 score on a validation set of 1600 sentences. | 42e7e8d4480e55d88496675936c2c824 |
other | ['generated_from_trainer'] | false | 6.7b-dalio-book-handwritten-io-cosine-6e-6 This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0586 - Accuracy: 0.3412 | e15bb3fe038af290432478198d8c3591 |
other | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 1.0 | b0ed1ab502184c7ec0f74d16bca3c0fe |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.6377 | 0.11 | 6 | 2.4688 | 0.3016 | | 2.5046 | 0.21 | 12 | 2.3848 | 0.3096 | | 2.4755 | 0.32 | 18 | 2.3223 | 0.3156 | | 2.459 | 0.43 | 24 | 2.2715 | 0.3201 | | 2.3602 | 0.54 | 30 | 2.2246 | 0.3243 | | 2.3829 | 0.64 | 36 | 2.1895 | 0.3275 | | 2.3188 | 0.75 | 42 | 2.1465 | 0.3315 | | 2.2895 | 0.86 | 48 | 2.1035 | 0.3365 | | 2.3062 | 0.96 | 54 | 2.0586 | 0.3412 | | 5adffb3072bb572f3f6127d943cc80e4 |
openrail | [] | false | Model Card for LoRA Based on a 48 image dataset scraped from Danbooru and tagged with WD1.4 Tagger. Trained for 30 epochs (7200 steps), best with models and merges based on Anything v3. Not particularly prone to NSFW, as the training dataset was somewhat balanced, but is capable of it. Outfits tend to Ruby's default colors of red and black unless specified otherwise, especially all kinds of dresses. I also recommend using Latent upscaler with medium (0.4-0.5) denoise, as it can fix some small inconsistencies like wrong eye color. | 58089b26dcf59822ff613621c9f5f9de |
openrail | [] | false | Model Description Version 1.0 is currently the only one available and is the least prone to straying from the prompt (white dress stays white), however, may be slightly inaccurate when depicting Ruby. Best weights seem to be in the area of 0.6 to 0.7, and for best results I recommend adding in tags like "grey eyes, red hair, multicolored hair". Higher weights can sometimes lead to facial artifacts and/or weird anatomy. - **Developed by:** DarkSolus - **Model type:** LoRA - **Finetuned from model [optional]:** Anything v3 | 3e5677f845205ec252ab9077454d46ef |
openrail | [] | false | How to Get Started with the Model Download the preferred version of the LoRA from the repo. Install Additional Networks extension: 1) via Auto1111's extension manager 2) via GitHub: https://github.com/kohya-ss/sd-webui-additional-networks Reload the UI, and place your downloaded LoRA into: .\stable-diffusion-webui\extensions\sd-webui-additional-networks\models\lora | b7e7d1bf06fa2324f46c0682dc23842a |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-med-term-conditional-masking This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6808 - Rouge2 Precision: 0.6855 - Rouge2 Recall: 0.486 - Rouge2 Fmeasure: 0.5507 | a9abf355b14fefd86bd6893460300244 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:------:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.9303 | 1.0 | 15827 | 0.8262 | 0.6603 | 0.4698 | 0.5318 | | 0.8677 | 2.0 | 31654 | 0.7679 | 0.6695 | 0.4762 | 0.539 | | 0.8315 | 3.0 | 47481 | 0.7393 | 0.6741 | 0.4783 | 0.5418 | | 0.7999 | 4.0 | 63308 | 0.7194 | 0.6774 | 0.4811 | 0.5448 | | 0.7746 | 5.0 | 79135 | 0.7059 | 0.6804 | 0.4815 | 0.5459 | | 0.7785 | 6.0 | 94962 | 0.6958 | 0.6827 | 0.4841 | 0.5485 | | 0.7592 | 7.0 | 110789 | 0.6893 | 0.6841 | 0.4849 | 0.5494 | | 0.745 | 8.0 | 126616 | 0.6849 | 0.6846 | 0.4852 | 0.5498 | | 0.7443 | 9.0 | 142443 | 0.6818 | 0.6854 | 0.4865 | 0.551 | | 0.7417 | 10.0 | 158270 | 0.6808 | 0.6855 | 0.486 | 0.5507 | | 3fe64a1cf92c22e6f7acf95d67b40118 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-base-squadshifts-vanilla-nyt-qg` This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: nyt) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | c481805a1be8eebe3dcbfe6b95637fda |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [t5-base](https://huggingface.co/t5-base) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (nyt) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) | c7286c29667e6bda1e84ba56a42e9846 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-base-squadshifts-vanilla-nyt-qg") output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` | e0016542caf84dbdbd92781554ee1938 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-base-squadshifts-vanilla-nyt-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.nyt.json) | | Score | Type | Dataset | |:-----------|--------:|:-------|:---------------------------------------------------------------------------| | BERTScore | 91.86 | nyt | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_1 | 22.57 | nyt | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_2 | 14.54 | nyt | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_3 | 10.04 | nyt | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_4 | 7.23 | nyt | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | METEOR | 22.29 | nyt | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | MoverScore | 62.68 | nyt | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | ROUGE_L | 22.67 | nyt | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | 93ecbfe93522fa49c6fbf0aa7ac21808 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: nyt - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-base - max_length: 512 - max_length_output: 32 - epoch: 8 - batch: 8 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 8 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/t5-base-squadshifts-vanilla-nyt-qg/raw/main/trainer_config.json). | c353b795e31f83f3d350ca19e0f738e2 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Marathi This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | a006ede30b070143d44f2d0c7996ddb1 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-mr") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-mr") ``` | 2a5b3e58d04835a7381515deaf422a73 |
cc-by-sa-4.0 | ['long-documents'] | false | Model description This is a Hierarchical Attention Transformer (HAT) model as presented in [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification (Chalkidis et al., 2022)](https://arxiv.org/abs/2210.05529). The model has been warm-started re-using the weights of RoBERTa (Liu et al., 2019), BUT has not been continued pre-trained. It supports sequences of length up to 4,096. HAT uses hierarchical attention, which is a combination of segment-wise and cross-segment attention operations. You can think of segments as paragraphs or sentences. Note: If you wish to use a fully pre-trained HAT model, you have to use [kiddothe2b/adhoc-hat-base-4096](https://huggingface.co/kiddothe2b/adhoc-hat-base-4096). | 3be6da664134b818bc196a9464bd220a |
cc-by-sa-4.0 | ['long-documents'] | false | Intended uses & limitations The model is intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=hierarchical-transformer) to look for other versions of HAT, or fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole document to make decisions, such as document classification, sequential sentence classification, or question answering. | 29784ce6acd4a6dfdadb99baaccd496a |
cc-by-sa-4.0 | ['long-documents'] | false | How to use You can fine-tune it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks: ```python from transformers import AutoTokenizer, AutoModelforSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/adhoc-hierarchical-transformer-base-4096", trust_remote_code=True) doc_classifier = AutoModelforSequenceClassification("kiddothe2b/adhoc-hierarchical-transformer-base-4096", trust_remote_code=True) ``` Note: If you wish to use a fully pre-trained HAT model, you have to use [kiddothe2b/hierarchical-transformer-base-4096](https://huggingface.co/kiddothe2b/hierarchical-transformer-base-4096). | d27b4b71eed9937fb3657ccdacd1874e |
apache-2.0 | ['generated_from_trainer'] | false | resnet-50-FV2-finetuned-memes This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.9263 - Accuracy: 0.6453 - Precision: 0.5728 - Recall: 0.6453 - F1: 0.5964 | 0418f25a658f93349920c83705a38ac9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.5763 | 0.99 | 20 | 1.5575 | 0.4281 | 0.2966 | 0.4281 | 0.2669 | | 1.4761 | 1.99 | 40 | 1.4424 | 0.4343 | 0.1886 | 0.4343 | 0.2630 | | 1.3563 | 2.99 | 60 | 1.3240 | 0.4343 | 0.1886 | 0.4343 | 0.2630 | | 1.2824 | 3.99 | 80 | 1.2636 | 0.4389 | 0.3097 | 0.4389 | 0.2734 | | 1.2315 | 4.99 | 100 | 1.2119 | 0.4529 | 0.3236 | 0.4529 | 0.3042 | | 1.1956 | 5.99 | 120 | 1.1764 | 0.4900 | 0.3731 | 0.4900 | 0.3692 | | 1.1452 | 6.99 | 140 | 1.1424 | 0.5147 | 0.3963 | 0.5147 | 0.4090 | | 1.1076 | 7.99 | 160 | 1.1190 | 0.5371 | 0.4121 | 0.5371 | 0.4392 | | 1.0679 | 8.99 | 180 | 1.0825 | 0.5719 | 0.4465 | 0.5719 | 0.4831 | | 1.0432 | 9.99 | 200 | 1.0482 | 0.5750 | 0.5404 | 0.5750 | 0.4930 | | 0.9903 | 10.99 | 220 | 1.0275 | 0.5958 | 0.5459 | 0.5958 | 0.5241 | | 0.9675 | 11.99 | 240 | 1.0145 | 0.6051 | 0.5350 | 0.6051 | 0.5379 | | 0.9335 | 12.99 | 260 | 0.9860 | 0.6175 | 0.5537 | 0.6175 | 0.5527 | | 0.9157 | 13.99 | 280 | 0.9683 | 0.6105 | 0.5386 | 0.6105 | 0.5504 | | 0.8901 | 14.99 | 300 | 0.9558 | 0.6352 | 0.5686 | 0.6352 | 0.5833 | | 0.8722 | 15.99 | 320 | 0.9382 | 0.6345 | 0.5657 | 0.6345 | 0.5807 | | 0.854 | 16.99 | 340 | 0.9322 | 0.6376 | 0.5623 | 0.6376 | 0.5856 | | 0.8494 | 17.99 | 360 | 0.9287 | 0.6422 | 0.6675 | 0.6422 | 0.5918 | | 0.8652 | 18.99 | 380 | 0.9212 | 0.6399 | 0.5640 | 0.6399 | 0.5863 | | 0.846 | 19.99 | 400 | 0.9263 | 0.6453 | 0.5728 | 0.6453 | 0.5964 | | 7682f92b9a19806ec7eb2c7f58b5454f |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | bart-base-finetuned-summarization-cnn-ver1.2 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 2.2476 - Bertscore-mean-precision: 0.8904 - Bertscore-mean-recall: 0.8611 - Bertscore-mean-f1: 0.8753 - Bertscore-median-precision: 0.8891 - Bertscore-median-recall: 0.8600 - Bertscore-median-f1: 0.8741 | e12db6418c4a490ca043ec1701b4a61c |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 9287973ca7c221cc61373051a8dc1e91 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bertscore-mean-precision | Bertscore-mean-recall | Bertscore-mean-f1 | Bertscore-median-precision | Bertscore-median-recall | Bertscore-median-f1 | |:-------------:|:-----:|:-----:|:---------------:|:------------------------:|:---------------------:|:-----------------:|:--------------------------:|:-----------------------:|:-------------------:| | 2.3305 | 1.0 | 5742 | 2.2125 | 0.8845 | 0.8587 | 0.8713 | 0.8840 | 0.8577 | 0.8706 | | 1.7751 | 2.0 | 11484 | 2.2028 | 0.8910 | 0.8616 | 0.8759 | 0.8903 | 0.8603 | 0.8744 | | 1.4564 | 3.0 | 17226 | 2.2476 | 0.8904 | 0.8611 | 0.8753 | 0.8891 | 0.8600 | 0.8741 | | 30f86d63c6e48aaa9977229e3708661a |
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: 0.0587 - Precision: 0.9333 - Recall: 0.9515 - F1: 0.9423 - Accuracy: 0.9871 | 39d6ef4517572e76c5d242b581015092 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.086 | 1.0 | 1756 | 0.0634 | 0.9186 | 0.9364 | 0.9274 | 0.9829 | | 0.0372 | 2.0 | 3512 | 0.0598 | 0.9328 | 0.9478 | 0.9402 | 0.9860 | | 0.0217 | 3.0 | 5268 | 0.0587 | 0.9333 | 0.9515 | 0.9423 | 0.9871 | | eb5d1f41499b3d847b0973f9c12985ea |
apache-2.0 | ['lexical normalization'] | false | Fine-tuned ByT5-small for MultiLexNorm (Croatian version)  This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://noisy-text.github.io/2021/multi-lexnorm.html), which evaluates lexical-normalization systems on 12 social media datasets in 11 languages. Our system is based on [ByT5](https://arxiv.org/abs/2105.13626), which we first pre-train on synthetic data and then fine-tune on authentic normalization data. It achieves the best performance by a wide margin in intrinsic evaluation, and also the best performance in extrinsic evaluation through dependency parsing. In addition to these fine-tuned models, we also release the source files on [GitHub](https://github.com/ufal/multilexnorm2021) and an interactive demo on [Google Colab](https://colab.research.google.com/drive/1rxpI8IlKk-D2crFqi2hdzbTBIezqgsCg?usp=sharing). | 73ba140ea7d6fcd1d36f7136d52e6f5a |
mit | [] | false | Description A fine-tuned regression model that assigns a functioning level to Dutch sentences describing exercise tolerance functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about exercise tolerance functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model. | 25d02cdb45689fd46b129342487cccd0 |
mit | [] | false | Functioning levels Level | Meaning ---|--- 5 | MET>6. Can tolerate jogging, hard exercises, running, climbing stairs fast, sports. 4 | 4≤MET≤6. Can tolerate walking / cycling at a brisk pace, considerable effort (e.g. cycling from 16 km/h), heavy housework. 3 | 3≤MET<4. Can tolerate walking / cycling at a normal pace, gardening, exercises without equipment. 2 | 2≤MET<3. Can tolerate walking at a slow to moderate pace, grocery shopping, light housework. 1 | 1≤MET<2. Can tolerate sitting activities. 0 | 0≤MET<1. Can physically tolerate only recumbent activities. The predictions generated by the model might sometimes be outside of the scale (e.g. 5.2); this is normal in a regression model. | 46cbfe2cbbeb514bbfa4c81a2f94c7ff |
mit | [] | false | How to use To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library: ``` from simpletransformers.classification import ClassificationModel model = ClassificationModel( 'roberta', 'CLTL/icf-levels-ins', use_cuda=False, ) example = 'kan nog goed traplopen, maar flink ingeleverd aan conditie na Corona' _, raw_outputs = model.predict([example]) predictions = np.squeeze(raw_outputs) ``` The prediction on the example is: ``` 3.13 ``` The raw outputs look like this: ``` [[3.1300993]] ``` | 57d7d5cd3442572c0280b49fdef63e3d |
mit | [] | false | Evaluation results The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals). | | Sentence-level | Note-level |---|---|--- mean absolute error | 0.69 | 0.61 mean squared error | 0.80 | 0.64 root mean squared error | 0.89 | 0.80 | 240edb40579afbebb099278fccecc400 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53-Total_2e-4_2 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2733 - Wer: 0.2116 | 841179107c85286ae71f33b55201c1f0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 5.2741 | 0.1 | 200 | 2.9070 | 0.9707 | | 2.034 | 0.2 | 400 | 0.7240 | 0.6798 | | 1.0037 | 0.3 | 600 | 0.5651 | 0.5368 | | 0.8834 | 0.4 | 800 | 0.4709 | 0.4669 | | 0.7973 | 0.5 | 1000 | 0.4305 | 0.4261 | | 0.7489 | 0.6 | 1200 | 0.4017 | 0.3763 | | 0.7507 | 0.7 | 1400 | 0.3662 | 0.3481 | | 0.7108 | 0.8 | 1600 | 0.3604 | 0.3513 | | 0.7151 | 0.9 | 1800 | 0.3563 | 0.3406 | | 0.6755 | 1.0 | 2000 | 0.3365 | 0.3210 | | 0.6038 | 1.1 | 2200 | 0.3394 | 0.3053 | | 0.6109 | 1.2 | 2400 | 0.3179 | 0.2844 | | 0.5999 | 1.3 | 2600 | 0.3166 | 0.2773 | | 0.6291 | 1.4 | 2800 | 0.3134 | 0.2733 | | 0.626 | 1.5 | 3000 | 0.3060 | 0.2690 | | 0.6188 | 1.6 | 3200 | 0.3038 | 0.2644 | | 0.5757 | 1.7 | 3400 | 0.3015 | 0.2566 | | 0.5943 | 1.8 | 3600 | 0.2925 | 0.2494 | | 0.6043 | 1.9 | 3800 | 0.2858 | 0.2491 | | 0.5874 | 2.0 | 4000 | 0.2874 | 0.2452 | | 0.5263 | 2.1 | 4200 | 0.2800 | 0.2364 | | 0.5282 | 2.2 | 4400 | 0.2848 | 0.2387 | | 0.4953 | 2.3 | 4600 | 0.2793 | 0.2360 | | 0.5428 | 2.4 | 4800 | 0.2863 | 0.2414 | | 0.5618 | 2.5 | 5000 | 0.2788 | 0.2350 | | 0.5395 | 2.6 | 5200 | 0.2765 | 0.2325 | | 0.5178 | 2.7 | 5400 | 0.2787 | 0.2351 | | 0.5264 | 2.8 | 5600 | 0.2755 | 0.2312 | | 0.5222 | 2.9 | 5800 | 0.2692 | 0.2258 | | 0.5184 | 3.0 | 6000 | 0.2681 | 0.2242 | | 0.4826 | 3.1 | 6200 | 0.2736 | 0.2224 | | 0.479 | 3.2 | 6400 | 0.2896 | 0.2353 | | 0.4938 | 3.3 | 6600 | 0.2744 | 0.2252 | | 0.4772 | 3.4 | 6800 | 0.2735 | 0.2242 | | 0.4831 | 3.5 | 7000 | 0.2721 | 0.2225 | | 0.4869 | 3.6 | 7200 | 0.2710 | 0.2194 | | 0.4515 | 3.7 | 7400 | 0.2692 | 0.2196 | | 0.4732 | 3.8 | 7600 | 0.2729 | 0.2269 | | 0.4683 | 3.9 | 7800 | 0.2713 | 0.2211 | | 0.4674 | 4.0 | 8000 | 0.2642 | 0.2116 | | 0.4239 | 4.1 | 8200 | 0.2773 | 0.2176 | | 0.4306 | 4.2 | 8400 | 0.2779 | 0.2191 | | 0.441 | 4.3 | 8600 | 0.2758 | 0.2136 | | 0.4343 | 4.4 | 8800 | 0.2797 | 0.2203 | | 0.4059 | 4.5 | 9000 | 0.2763 | 0.2159 | | 0.4399 | 4.6 | 9200 | 0.2755 | 0.2123 | | 0.4131 | 4.7 | 9400 | 0.2741 | 0.2124 | | 0.4331 | 4.8 | 9600 | 0.2728 | 0.2101 | | 0.4288 | 4.9 | 9800 | 0.2730 | 0.2110 | | 0.4341 | 5.0 | 10000 | 0.2733 | 0.2116 | | 9269bbb01d7cda96c0bd88a78c5b2e7f |
mit | ['keyphrase-extraction'] | false | 🔑 Keyphrase Extraction Model: KBIR-KPCrowd Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done primarily by human annotators, who read the text in detail and then wrote down the most important keyphrases. The disadvantage is that if you work with a lot of documents, this process can take a lot of time ⏳. Here is where Artificial Intelligence 🤖 comes in. Currently, classical machine learning methods, that use statistical and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency, occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies and context of words in a text. | 48ba9167928337a07190ae6f654f4f4d |
mit | ['keyphrase-extraction'] | false | 📓 Model Description This model uses [KBIR](https://huggingface.co/bloomberg/KBIR) as its base model and fine-tunes it on the [KPCrowd dataset](https://huggingface.co/datasets/midas/kpcrowd). KBIR or Keyphrase Boundary Infilling with Replacement is a pre-trained model which utilizes a multi-task learning setup for optimizing a combined loss of Masked Language Modeling (MLM), Keyphrase Boundary Infilling (KBI) and Keyphrase Replacement Classification (KRC). You can find more information about the architecture in this [paper](https://arxiv.org/abs/2112.08547). Keyphrase extraction models are transformer models fine-tuned as a token classification problem where each word in the document is classified as being part of a keyphrase or not. | Label | Description | | ----- | ------------------------------- | | B-KEY | At the beginning of a keyphrase | | I-KEY | Inside a keyphrase | | O | Outside a keyphrase | Kulkarni, Mayank, Debanjan Mahata, Ravneet Arora, and Rajarshi Bhowmik. "Learning Rich Representation of Keyphrases from Text." arXiv preprint arXiv:2112.08547 (2021). Sahrawat, Dhruva, Debanjan Mahata, Haimin Zhang, Mayank Kulkarni, Agniv Sharma, Rakesh Gosangi, Amanda Stent, Yaman Kumar, Rajiv Ratn Shah, and Roger Zimmermann. "Keyphrase extraction as sequence labeling using contextualized embeddings." In European Conference on Information Retrieval, pp. 328-335. Springer, Cham, 2020. | 137f21f21151c68ef64533a4ea793a26 |
mit | ['keyphrase-extraction'] | false | 🛑 Limitations * This keyphrase extraction model is very dataset-specific. It's not recommended to use this model for other domains, but you are free to test it out. * Only works for English documents. * Large number of annotated keyphrases. | af1785cf86fc2eb97b1d2f65a5c56ded |
mit | ['keyphrase-extraction'] | false | Define keyphrase extraction pipeline class KeyphraseExtractionPipeline(TokenClassificationPipeline): def __init__(self, model, *args, **kwargs): super().__init__( model=AutoModelForTokenClassification.from_pretrained(model), tokenizer=AutoTokenizer.from_pretrained(model), *args, **kwargs ) def postprocess(self, model_outputs): results = super().postprocess( model_outputs=model_outputs, aggregation_strategy=AggregationStrategy.SIMPLE, ) return np.unique([result.get("word").strip() for result in results]) ``` ```python | 8271ec0b0611e4b129d05ed8e6a91b76 |
mit | ['keyphrase-extraction'] | false | Inference text = """ Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done primarily by human annotators, who read the text in detail and then wrote down the most important keyphrases. The disadvantage is that if you work with a lot of documents, this process can take a lot of time. Here is where Artificial Intelligence comes in. Currently, classical machine learning methods, that use statistical and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency, occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies and context of words in a text. """.replace("\n", " ") keyphrases = extractor(text) print(keyphrases) ``` ``` | a93be4a58f57b02a319c835996c12006 |
mit | ['keyphrase-extraction'] | false | Output ['Artificial Intelligence' 'Classical' 'Keyphrase' 'Keyphrase extraction' 'classical' 'content' 'context' 'disadvantage' 'document' 'documents' 'extract' 'extraction' 'extraction process' 'frequency' 'human' 'humans' 'important' 'keyphrases' 'learning' 'linguistic' 'long-term' 'machine learning' 'meaning' 'methods' 'neural approaches' 'occurrence' 'process' 'quickly' 'semantic' 'statistical' 'technique' 'text' 'text analysis' 'understand' 'widely' 'words' 'work'] ``` | 628aae91a40203a4ac76acc2053485cf |
mit | ['keyphrase-extraction'] | false | 📚 Training Dataset [KPCrowd](https://huggingface.co/datasets/midas/kpcrowd) is a broadcast news transcription dataset consisting of 500 English broadcast news stories from 10 different categories (art and culture, business, crime, fashion, health, politics us, politics world, science, sports, technology) with 50 docs per category. This dataset is annotated by multiple annotators that were required to look at the same news story and assign a set of keyphrases from the text itself. You can find more information in the [paper](https://arxiv.org/abs/1306.4606). | 92c0deea25cce59b0919df78dc297c0f |
mit | ['keyphrase-extraction'] | false | Dataset parameters dataset_full_name = "midas/kpcrowd" dataset_subset = "raw" dataset_document_column = "document" dataset_biotags_column = "doc_bio_tags" def preprocess_fuction(all_samples_per_split): tokenized_samples = tokenizer.batch_encode_plus( all_samples_per_split[dataset_document_column], padding="max_length", truncation=True, is_split_into_words=True, max_length=max_length, ) total_adjusted_labels = [] for k in range(0, len(tokenized_samples["input_ids"])): prev_wid = -1 word_ids_list = tokenized_samples.word_ids(batch_index=k) existing_label_ids = all_samples_per_split[dataset_biotags_column][k] i = -1 adjusted_label_ids = [] for wid in word_ids_list: if wid is None: adjusted_label_ids.append(lbl2idx["O"]) elif wid != prev_wid: i = i + 1 adjusted_label_ids.append(lbl2idx[existing_label_ids[i]]) prev_wid = wid else: adjusted_label_ids.append( lbl2idx[ f"{'I' if existing_label_ids[i] == 'B' else existing_label_ids[i]}" ] ) total_adjusted_labels.append(adjusted_label_ids) tokenized_samples["labels"] = total_adjusted_labels return tokenized_samples | 467f95ab1cc63e31b2879ac596e4225e |
mit | ['keyphrase-extraction'] | false | 📝 Evaluation results Traditional evaluation methods are the precision, recall and F1-score @k,m where k is the number that stands for the first k predicted keyphrases and m for the average amount of predicted keyphrases. The model achieves the following results on the Inspec test set: | Dataset | P@5 | R@5 | F1@5 | P@10 | R@10 | F1@10 | P@M | R@M | F1@M | |:-----------------:|:----:|:----:|:----:|:----:|:----:|:-----:|:----:|:----:|:----:| | Inspec Test Set | 0.47 | 0.07 | 0.12 | 0.46 | 0.13 | 0.20 | 0.37 | 0.33 | 0.33 | | 5cee6ac402c81f6457cfc1c2927af21c |
mit | [] | false | Model description **bert-base-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). Specifically, this model is a *bert-base-cased* model that was fine-tuned on the English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset. If you'd like to use a larger BERT-large model fine-tuned on the same dataset, a [**bert-large-NER**](https://huggingface.co/dslim/bert-large-NER/) version is also available. | f48f6327d8023c8f682bcf8cef2277da |
mit | [] | false | Eval results metric|dev|test -|-|- f1 |95.1 |91.3 precision |95.0 |90.7 recall |95.3 |91.9 The test metrics are a little lower than the official Google BERT results which encoded document context & experimented with CRF. More on replicating the original results [here](https://github.com/google-research/bert/issues/223). | a24d6b00aed9b9960da522f99c94a73d |
other | ['generated_from_trainer'] | false | nvidia-segformer-b0-finetuned-ade-512-512-finetuned-ISIC17 This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1948 - Mean Iou: 0.8064 - Mean Accuracy: 0.8726 - Overall Accuracy: 0.9381 - Per Category Iou: [0.6841604127643356, 0.9285439643646547] - Per Category Accuracy: [0.7721651141608432, 0.9729809595315688] | dcfd12e6948ea7ea78f0911b33a3c08d |
other | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | a73446e59f616035353e4d048d000bce |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------------------------------:|:-----------------------------------------:| | 0.481 | 0.16 | 10 | 0.4235 | 0.6191 | 0.6970 | 0.8761 | [0.3719409076673884, 0.8662862424406493] | [0.42270204900152314, 0.9713331864930521] | | 0.4147 | 0.32 | 20 | 0.3894 | 0.7067 | 0.8502 | 0.8853 | [0.5464942438498753, 0.8668431573745645] | [0.7965579529885418, 0.9038859083170013] | | 0.356 | 0.48 | 30 | 0.3148 | 0.7467 | 0.8513 | 0.9107 | [0.5963581593534901, 0.897077797385972] | [0.7603709174964982, 0.9422313184595918] | | 0.3039 | 0.63 | 40 | 0.3024 | 0.7620 | 0.8671 | 0.9162 | [0.6211722830632663, 0.9028139512386881] | [0.7918407335685692, 0.9422883932404167] | | 0.2545 | 0.79 | 50 | 0.2849 | 0.7766 | 0.8898 | 0.9201 | [0.6468577863419183, 0.9063792530493855] | [0.8432862096150755, 0.9362151542385662] | | 0.2635 | 0.95 | 60 | 0.2504 | 0.7828 | 0.8644 | 0.9279 | [0.6487213857926865, 0.9168129696986418] | [0.7671470887645524, 0.9616549114054705] | | 0.2175 | 1.11 | 70 | 0.2497 | 0.7849 | 0.8682 | 0.9283 | [0.6526705030304356, 0.9171225024239068] | [0.7762677096648272, 0.9602225755678137] | | 0.2025 | 1.27 | 80 | 0.2400 | 0.7840 | 0.8632 | 0.9288 | [0.6501844204669202, 0.9178944798865282] | [0.7627291445016801, 0.9636411137781736] | | 0.2035 | 1.43 | 90 | 0.2288 | 0.7931 | 0.8749 | 0.9313 | [0.6657367286733036, 0.9203778068784213] | [0.7885027822639286, 0.9612655167036179] | | 0.2488 | 1.59 | 100 | 0.2110 | 0.7978 | 0.8719 | 0.9341 | [0.6717638717220313, 0.923859975121704] | [0.7766611302038285, 0.9672003292652145] | | 0.1954 | 1.75 | 110 | 0.2067 | 0.7962 | 0.8597 | 0.9354 | [0.666599427783381, 0.9258672754383861] | [0.7436428904928473, 0.9757231213956472] | | 0.1806 | 1.9 | 120 | 0.2047 | 0.7926 | 0.8525 | 0.9349 | [0.6596059897565958, 0.925563006736469] | [0.726197674685608, 0.9787940661520825] | | 0.161 | 2.06 | 130 | 0.2047 | 0.7903 | 0.8505 | 0.9342 | [0.6558737849234609, 0.9247714617107691] | [0.7223974159771602, 0.9786951901233297] | | 0.1736 | 2.22 | 140 | 0.2023 | 0.7948 | 0.8588 | 0.9349 | [0.6643652721485811, 0.9252950591002775] | [0.742124317828686, 0.9754152391272543] | | 0.1947 | 2.38 | 150 | 0.2077 | 0.7985 | 0.8656 | 0.9355 | [0.6712414223331253, 0.9257326708494226] | [0.7585178608332249, 0.9726888331181641] | | 0.1464 | 2.54 | 160 | 0.1960 | 0.8030 | 0.8680 | 0.9373 | [0.678274892507806, 0.9276935390097538] | [0.7620104248788739, 0.9740685958478499] | | 0.1644 | 2.7 | 170 | 0.1964 | 0.8064 | 0.8751 | 0.9377 | [0.6847175060674714, 0.9279857318627613] | [0.7791196258677832, 0.9710404169835255] | | 0.1803 | 2.86 | 180 | 0.1948 | 0.8064 | 0.8726 | 0.9381 | [0.6841604127643356, 0.9285439643646547] | [0.7721651141608432, 0.9729809595315688] | | 2c704e8b8bafd0ba3e5beb4bd1dc097e |
other | [] | false | DistilROBERTA fine-tuned for bias detection This model is based on [distilroberta-base](https://huggingface.co/distilroberta-base) pretrained weights, with a classification head fine-tuned to classify text into 2 categories (neutral, biased). | 64817a0de47f5f97deaef6f6e0c269cd |
other | [] | false | Training data The dataset used to fine-tune the model is [wikirev-bias](https://huggingface.co/datasets/valurank/wikirev-bias), extracted from English wikipedia revisions, see https://github.com/rpryzant/neutralizing-bias for details on the WNC wiki edits corpus. | d175b7c93b981e1eb0534cc0636af73e |
apache-2.0 | ['generated_from_trainer'] | false | data-augmentation-whitenoise-timit-2310 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5916 - Wer: 0.3408 | cd9407439bcce52fbcbdf7c0b4b89ced |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.6731 | 0.67 | 500 | 2.7553 | 1.0 | | 1.0656 | 1.34 | 1000 | 0.5963 | 0.5297 | | 0.5065 | 2.01 | 1500 | 0.4898 | 0.4654 | | 0.3212 | 2.68 | 2000 | 0.4265 | 0.4331 | | 0.2492 | 3.35 | 2500 | 0.4020 | 0.4073 | | 0.2116 | 4.02 | 3000 | 0.4152 | 0.3935 | | 0.1719 | 4.69 | 3500 | 0.4258 | 0.3858 | | 0.1544 | 5.36 | 4000 | 0.4542 | 0.3818 | | 0.1474 | 6.03 | 4500 | 0.4612 | 0.3821 | | 0.1248 | 6.7 | 5000 | 0.4813 | 0.3749 | | 0.1148 | 7.37 | 5500 | 0.5131 | 0.3772 | | 0.1145 | 8.04 | 6000 | 0.5383 | 0.3714 | | 0.0986 | 8.71 | 6500 | 0.5288 | 0.3777 | | 0.091 | 9.38 | 7000 | 0.5071 | 0.3869 | | 0.0789 | 10.05 | 7500 | 0.5256 | 0.3819 | | 0.0747 | 10.72 | 8000 | 0.5287 | 0.3711 | | 0.0687 | 11.39 | 8500 | 0.5179 | 0.3754 | | 0.072 | 12.06 | 9000 | 0.7438 | 0.3702 | | 0.0646 | 12.73 | 9500 | 0.5293 | 0.3777 | | 0.0621 | 13.4 | 10000 | 0.5536 | 0.3692 | | 0.0587 | 14.08 | 10500 | 0.5214 | 0.3712 | | 0.0538 | 14.75 | 11000 | 0.4853 | 0.3694 | | 0.0614 | 15.42 | 11500 | 0.5439 | 0.3637 | | 0.0493 | 16.09 | 12000 | 0.5087 | 0.3649 | | 0.0441 | 16.76 | 12500 | 0.5736 | 0.3621 | | 0.038 | 17.43 | 13000 | 0.7295 | 0.3650 | | 0.0397 | 18.1 | 13500 | 0.5722 | 0.3586 | | 0.0357 | 18.77 | 14000 | 0.5701 | 0.3616 | | 0.0349 | 19.44 | 14500 | 0.5661 | 0.3599 | | 0.0318 | 20.11 | 15000 | 0.5346 | 0.3572 | | 0.0288 | 20.78 | 15500 | 0.6972 | 0.3597 | | 0.0331 | 21.45 | 16000 | 0.5288 | 0.3576 | | 0.0304 | 22.12 | 16500 | 0.5813 | 0.3551 | | 0.0268 | 22.79 | 17000 | 0.5439 | 0.3557 | | 0.0255 | 23.46 | 17500 | 0.5790 | 0.3531 | | 0.0244 | 24.13 | 18000 | 0.5794 | 0.3493 | | 0.0335 | 24.8 | 18500 | 0.5943 | 0.3515 | | 0.026 | 25.47 | 19000 | 0.5737 | 0.3462 | | 0.0199 | 26.14 | 19500 | 0.5794 | 0.3469 | | 0.0213 | 26.81 | 20000 | 0.5955 | 0.3448 | | 0.0199 | 27.48 | 20500 | 0.5927 | 0.3407 | | 0.0143 | 28.15 | 21000 | 0.5975 | 0.3415 | | 0.0167 | 28.82 | 21500 | 0.5835 | 0.3411 | | 0.0141 | 29.49 | 22000 | 0.5916 | 0.3408 | | 78355aefbc616ec630156e22b75f4c48 |
apache-2.0 | ['automatic-speech-recognition', 'th'] | false | exp_w2v2t_th_wavlm_s108 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition on Thai using the train split of [Common Voice 7.0](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. | 446b9cce9573ad75c451684c4c981ca2 |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-cola This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.9395 - Matthews Correlation: 0.6295 | 06d8e06f8c3e8539e4772bde559505e4 |
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