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 |... | 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/gith... | 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 Le... | 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 ... | 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-te... | 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 Me... | 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... | 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 |... | 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... | 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 |... | 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-tune... | 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("o... | 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_coll... | 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:// ... | 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.... | 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. ... | 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: 0.575... | 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 | 0.42... | 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 ... | 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 outp... | 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-commu... | 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]: h... | 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 | |... | 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_avai... | 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 | ... | 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', 'scor... | 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-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.... | 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 defaul... | 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,... | 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\... | 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... | 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... | 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... | 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-squadshif... | 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 ... | 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: ... | 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 (L... | 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-tun... | 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", tru... | 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 |... | 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 - ... | 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 | |:-------------:|:-----:|:-----:|:---------------:|:------------------------:|:---------------------:... | 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 |... | 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://no... | 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 sentenc... | 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... | 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 traplope... | 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 erro... | 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 | 0.536... | 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 ... | 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 opt... | 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), ... | 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 anno... | 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' '... | 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... | 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], ... | 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... | 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). Sp... | 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/22... | 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... | 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------... | 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 | 0.465... | 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 a... | 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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