license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | [] | false | Abstractive Summarization ``` from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("bloomberg/KeyBART") model = AutoModelForSeq2SeqLM.from_pretrained("bloomberg/KeyBART") from datasets import load_dataset dataset = load_dataset("cnn_dailymail") ``` Reported Results: | Model | R1 | R2 | RL | |--------------|-------|-------|-------| | BART (Lewis et al., 2019) | 44.16 | 21.28 | 40.9 | | BART* | 42.93 | 20.12 | 39.72 | | KeyBART-DOC* | 42.92 | 20.07 | 39.69 | | KeyBART* | 43.10 | 20.26 | 39.90 | | 2c6eccd0256e4c488174549eebf2f6fd |
apache-2.0 | [] | false | Zero-shot settings ``` from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("bloomberg/KeyBART") model = AutoModelForSeq2SeqLM.from_pretrained("bloomberg/KeyBART") ``` Alternatively use the Hosted Inference API console provided in https://huggingface.co/bloomberg/KeyBART Sample Zero Shot result: ``` Input: In this work, we explore how to learn task specific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for pre-training transformer language models (LMs) in discriminative as well as generative settings. In the discriminative setting, we introduce a new pre-training objective - Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains in performance (upto 9.26 points in F1) over SOTA, when LM pre-trained using KBIR is fine-tuned for the task of keyphrase extraction. In the generative setting, we introduce a new pre-training setup for BART - KeyBART, that reproduces the keyphrases related to the input text in the CatSeq format, instead of the denoised original input. This also led to gains in performance (upto 4.33 points in F1@M) over SOTA for keyphrase generation. Additionally, we also fine-tune the pre-trained language models on named entity recognition (NER), question answering (QA), relation extraction (RE), abstractive summarization and achieve comparable performance with that of the SOTA, showing that learning rich representation of keyphrases is indeed beneficial for many other fundamental NLP tasks. Output: language model;keyphrase generation;new pre-training objective;pre-training setup; ``` | d9d5ef877bbd6dc77ee932600f26a489 |
apache-2.0 | [] | false | Citation Please cite this work using the following BibTeX entry: ``` @inproceedings{kulkarni-etal-2022-learning, title = "Learning Rich Representation of Keyphrases from Text", author = "Kulkarni, Mayank and Mahata, Debanjan and Arora, Ravneet and Bhowmik, Rajarshi", booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.findings-naacl.67", doi = "10.18653/v1/2022.findings-naacl.67", pages = "891--906", abstract = "In this work, we explore how to train task-specific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for pre-training transformer language models (LMs) in discriminative as well as generative settings. In the discriminative setting, we introduce a new pre-training objective - Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains in performance (upto 8.16 points in F1) over SOTA, when the LM pre-trained using KBIR is fine-tuned for the task of keyphrase extraction. In the generative setting, we introduce a new pre-training setup for BART - KeyBART, that reproduces the keyphrases related to the input text in the CatSeq format, instead of the denoised original input. This also led to gains in performance (upto 4.33 points in F1@M) over SOTA for keyphrase generation. Additionally, we also fine-tune the pre-trained language models on named entity recognition (NER), question answering (QA), relation extraction (RE), abstractive summarization and achieve comparable performance with that of the SOTA, showing that learning rich representation of keyphrases is indeed beneficial for many other fundamental NLP tasks.", } ``` Please direct all questions to dpreotiucpie@bloomberg.net | 98d908b18eb430b062e69fc50b9d94ed |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned_emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3916 - Accuracy: 0.886 - F1: 0.8818 | 1748b3764781ee7f0ce811238b85c300 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 125 | 0.6487 | 0.7875 | 0.7547 | | 0.8271 | 2.0 | 250 | 0.3916 | 0.886 | 0.8818 | | facad25e2d9404325294a3145f029f38 |
apache-2.0 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_vp-sv_s507 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pl)](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. | 40c3f13725c31aea245d79371c80fe7f |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4436448/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 2bb79a04369401f5e2ed978187238463 |
mit | [] | false | TomCat on Stable Diffusion This is the `<tom-cat>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:      | bd062e5810d5592e159e4f84a80b51ba |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True, 'skip_tokens': 1649999872}, 'generation': {'batch_size': 128, 'every_n_steps': 512, 'force_call_on': [12588], 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 640, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_hits_threshold': 0, 'num_samples': 2048}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_hits_threshold': 0, 'num_samples': 2048, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'every_n_steps': 512, 'force_call_on': [12588], 'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': 'c38e2b6acf17781918d39a310ee1adc4674a8225', 'value_head_config': {'is_detached': False}}, 'path_or_name': 'kejian/mighty-rwr'}, 'objective': {'alpha': 1, 'beta': 10, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 128, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'curious-rwr', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 12588, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1649999872, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | e333af3254555477d2178e9a6d56b9b2 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-9front-1body-9rear This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4361 - Wer: 0.1687 - Mer: 0.1630 - Wil: 0.2486 - Wip: 0.7514 - Hits: 55941 - Substitutions: 6292 - Deletions: 2354 - Insertions: 2252 - Cer: 0.1338 | e8945cecc42ecb2ee46b588ef39038eb |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.6124 | 1.0 | 1457 | 0.4613 | 0.2407 | 0.2209 | 0.3091 | 0.6909 | 54843 | 6758 | 2986 | 5804 | 0.2153 | | 0.4968 | 2.0 | 2914 | 0.4171 | 0.1777 | 0.1716 | 0.2580 | 0.7420 | 55404 | 6354 | 2829 | 2293 | 0.1402 | | 0.4817 | 3.0 | 4371 | 0.4129 | 0.1731 | 0.1673 | 0.2534 | 0.7466 | 55636 | 6332 | 2619 | 2227 | 0.1349 | | 0.4257 | 4.0 | 5828 | 0.4089 | 0.1722 | 0.1659 | 0.2520 | 0.7480 | 55904 | 6346 | 2337 | 2437 | 0.1361 | | 0.3831 | 5.0 | 7285 | 0.4144 | 0.1705 | 0.1646 | 0.2508 | 0.7492 | 55868 | 6343 | 2376 | 2290 | 0.1358 | | 0.3057 | 6.0 | 8742 | 0.4198 | 0.1690 | 0.1632 | 0.2492 | 0.7508 | 55972 | 6333 | 2282 | 2298 | 0.1350 | | 0.2919 | 7.0 | 10199 | 0.4220 | 0.1693 | 0.1635 | 0.2492 | 0.7508 | 55936 | 6310 | 2341 | 2281 | 0.1337 | | 0.2712 | 8.0 | 11656 | 0.4252 | 0.1688 | 0.1632 | 0.2487 | 0.7513 | 55905 | 6286 | 2396 | 2218 | 0.1348 | | 0.2504 | 9.0 | 13113 | 0.4332 | 0.1685 | 0.1629 | 0.2482 | 0.7518 | 55931 | 6270 | 2386 | 2226 | 0.1331 | | 0.2446 | 10.0 | 14570 | 0.4361 | 0.1687 | 0.1630 | 0.2486 | 0.7514 | 55941 | 6292 | 2354 | 2252 | 0.1338 | | 1e81a7b3b96cc33afec7dc1224dc8fb7 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/libritts_tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss` ♻️ Imported from https://zenodo.org/record/4418754/ This model was trained by kan-bayashi using libritts/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | aa72eaa912069fb94663514f737dde4b |
apache-2.0 | ['translation'] | false | opus-mt-en-uk * source languages: en * target languages: uk * OPUS readme: [en-uk](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-uk/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-uk/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-uk/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-uk/opus-2020-01-08.eval.txt) | 58df04f0df3967eff1390f90dc644046 |
apache-2.0 | ['generated_from_keras_callback'] | false | vinitharaj/distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.5718 - Validation Loss: 4.2502 - Epoch: 1 | 2b436880f03affdc4802853ac9947132 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 46, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | 0ab7f1750d3114d53594f0946ee6b959 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-large-squad-qg-no-paragraph` This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). This model is fine-tuned without pargraph information but only the sentence that contains the answer. | 80ff387a8cac2c5bc64f0e81d0c9a799 |
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/bart-large-squad-qg-no-paragraph") output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` | eb4c46fdf1fee432880478c4383b4585 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-large-squad-qg-no-paragraph/raw/main/eval/metric.first.sentence.sentence_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:---------------------------------------------------------------| | BERTScore | 90.7 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_1 | 55.85 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_2 | 39.85 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_3 | 30.44 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_4 | 23.86 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | METEOR | 25.18 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | MoverScore | 63.85 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | ROUGE_L | 51.43 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | b5fc329a2b8d2d14f316404bc0159a50 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['sentence_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-large - max_length: 128 - max_length_output: 32 - epoch: 8 - batch: 32 - lr: 5e-05 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 16 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/bart-large-squad-qg-no-paragraph/raw/main/trainer_config.json). | a0ab6b27bf54ff530a7b131d1b5f9135 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the Wall-E-01 concept trained by DiamondYin. This is a Stable Diffusion model fine-tuned on the Wall-E-01 concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of Wall-E-01 robot** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! | 4a1005c0d0a4dfed85745d802984c88c |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | Description This is a Stable Diffusion model fine-tuned on `robot` images for the wildcard theme, for the Hugging Face DreamBooth Hackathon, from the HF CN Community, corporated with the HeyWhale. The production cost of WALL-E is $180 million. It tells about a lonely robot designed to clean up the polluted earth. The unique feature of this film is that there is almost no dialogue in the first 40 minutes or so. On the contrary, the audience enters a world of robots; How it thinks, how it works, how it speaks (or doesn't speak). Pixar's classic film was a success. The film has a global box office of more than 520 million US dollars, won a number of Oscar nominations, and ranked first on Time magazine's list of the best films of the decade. Now we can easily create Wally's pictures and present the script's pictures with the help of the Stable Diffusion model. We can write a series of stories for WALL-E, but we don't have to bear such expensive costs. This is the advantage of the Stable Diffusion model 机器总动员这部电影（WALL-E）的生产成本为1.8亿美元。它讲述了一个孤独的机器人被设计来清理被污染的地球。这部电影的独特之处在于，前40分钟左右几乎没有对话，相反，观众进入了一个机器人的世界；它如何思考，如何工作，如何说话（或不说话）。皮克斯的经典电影获得了成功。 该片全球票房超过5.2亿美元，获得多项奥斯卡提名，并在《时代》杂志十年最佳影片排行榜上排名第一。现在，我们可以通过Stable Diffusion model轻松创建WALL-E的图片并呈现脚本的图片。我们可以为WALL-E写一系列故事，但我们不必承担如此昂贵的成本。这是稳定扩散模型的优点 下面是相关实例，大家可以体验。 调用时请注意主体的名称是：Wall-E-01 robot When calling, please note that the name of the subject is: Wall-E-01 robot Prompt: Wall-E-01 robot on the moon 8K resolution, 16:9,Cyberpunk   Prompt: Wall-E-01 robot, the background is an old bridge and a pond, mist and swirly clouds in the background, fantastic landscape, hyperrealism, no blur, 4k resolution, ultra detailed, style of Anton Fadeev, Ivan Shishkin, John Berkey  Prompt: illustration of a Wall-E robot sitting on top of the deck of a battle ship traveling through the open sea  Prompt: Wall-E-01 robot cartoon image with rainbow background    Prompt:"Wall-E, a small robot with a binocular-shaped head, sitting in the cockpit of a large spaceship, surrounded by high-tech controls and screens displaying various information about the ship's status and location, with a focus on Wall-E's expression and the intricate details of the ship's controls. The image should be in high resolution and have a realistic, futuristic aesthetic."    | 4e53a1f8f1719e05be20863052cbc322 |
apache-2.0 | ['visual-question-answering'] | false | Vision-and-Language Transformer (ViLT), fine-tuned on VQAv2 Vision-and-Language Transformer (ViLT) model fine-tuned on [VQAv2](https://visualqa.org/). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT). Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team. | 98b2712482e5be52f53da76ad87a7191 |
apache-2.0 | ['visual-question-answering'] | false | prepare image + question url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) text = "How many cats are there?" processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa") model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa") | 24581b87b6fd0780850700fde7e684d7 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | elyyoorrcchh Dreambooth model trained by Jorgitosch with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept: | c4d83c0f0522684f8e77ea248b0c481d |
apache-2.0 | ['generated_from_trainer'] | false | tiny-bert-sst2-distilled This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.7305 - Accuracy: 0.8326 | c201e2c6bcf063e59b14beca31f2c2eb |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0007199555649276667 - train_batch_size: 1024 - eval_batch_size: 1024 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 - mixed_precision_training: Native AMP | f241b02c269a5e2e4ac3304dac4844a7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.77 | 1.0 | 66 | 1.6939 | 0.8165 | | 0.729 | 2.0 | 132 | 1.5090 | 0.8326 | | 0.5242 | 3.0 | 198 | 1.5369 | 0.8257 | | 0.4017 | 4.0 | 264 | 1.7025 | 0.8326 | | 0.327 | 5.0 | 330 | 1.6743 | 0.8245 | | 0.2749 | 6.0 | 396 | 1.7305 | 0.8337 | | 0.2521 | 7.0 | 462 | 1.7305 | 0.8326 | | 8915c5808b513b940bf866b3b4180031 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2256 - Accuracy: 0.923 - F1: 0.9226 | a015fee0b5f9bfc7b883c76ba035549c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.807 | 1.0 | 250 | 0.3202 | 0.8995 | 0.8968 | | 0.2491 | 2.0 | 500 | 0.2256 | 0.923 | 0.9226 | | e055cdeeb9f409506af3c6918b18318c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | wav2vec2-large-xlsr-53-hebrew Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the several downloaded youtube samples. When using this model, make sure that your speech input is sampled at 16kHz. | 648607ce519fb796e524fbe9f400bbbd |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "he", split="test[:2%]") | 71c6e4824a7e110faaac5e042e710785 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | there is no common dataset for Hebrew, please, paste your data processor = Wav2Vec2Processor.from_pretrained("imvladikon/wav2vec2-large-xlsr-53-hebrew") model = Wav2Vec2ForCTC.from_pretrained("imvladikon/wav2vec2-large-xlsr-53-hebrew") resampler = torchaudio.transforms.Resample(48_000, 16_000) | 9835dc581f23f33991658c00fbae4a2e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` | 2431ac0838c846fb4417b857026a4638 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on some Hebrew test data ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "he", split="test") | 7c9c3b22824ab766433c4e525751d9a4 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | there is no common dataset for Hebrew, please, paste your data wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("imvladikon/wav2vec2-large-xlsr-53-hebrew") model = Wav2Vec2ForCTC.from_pretrained("imvladikon/wav2vec2-large-xlsr-53-hebrew").to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) | 09baa3bd6f3b0e517d7653dfd7212370 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: | aca635f7a6f2068815c2d1d343d1fcbf |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_wnli This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 2.4231 - Accuracy: 0.0845 | cc1aa466f007d2fccc9280a79c2456b4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6166 | 1.0 | 218 | 2.4231 | 0.0845 | | 0.4183 | 2.0 | 436 | 4.2000 | 0.0986 | | 0.3033 | 3.0 | 654 | 5.7862 | 0.0704 | | 0.2294 | 4.0 | 872 | 7.2969 | 0.0704 | | 0.1768 | 5.0 | 1090 | 7.5620 | 0.0986 | | 0.1365 | 6.0 | 1308 | 7.3554 | 0.0845 | | 0fb4a15bff8d96bebce09ffaa1d785e9 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-distilroberta-Is This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.7427 | b45905973709f100bc9cfd6c2e008edc |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 - mixed_precision_training: Native AMP | 86da6716478087d9d24266a08c562bd9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 19.6191 | 1.0 | 2135 | 10.5217 | | 8.6838 | 2.0 | 4270 | 7.3017 | | 6.884 | 3.0 | 6405 | 6.4445 | | 6.2953 | 4.0 | 8540 | 6.0610 | | 6.0205 | 5.0 | 10675 | 5.9047 | | 5.851 | 6.0 | 12810 | 5.7790 | | 5.7464 | 7.0 | 14945 | 5.7164 | | 5.6684 | 8.0 | 17080 | 5.6415 | | 5.6138 | 9.0 | 19215 | 5.5671 | | 5.5638 | 10.0 | 21350 | 5.5360 | | 5.5288 | 11.0 | 23485 | 5.5069 | | 5.4968 | 12.0 | 25620 | 5.4968 | | 5.4696 | 13.0 | 27755 | 5.4539 | | 5.4468 | 14.0 | 29890 | 5.4416 | | 5.4177 | 15.0 | 32025 | 5.3722 | | 5.3717 | 16.0 | 34160 | 5.3226 | | 5.317 | 17.0 | 36295 | 5.2197 | | 5.2367 | 18.0 | 38430 | 5.0888 | | 5.1543 | 19.0 | 40565 | 4.9954 | | 5.0919 | 20.0 | 42700 | 4.9306 | | 5.038 | 21.0 | 44835 | 4.8657 | | 4.9983 | 22.0 | 46970 | 4.8137 | | 4.9639 | 23.0 | 49105 | 4.7704 | | 4.9426 | 24.0 | 51240 | 4.7486 | | 4.9312 | 25.0 | 53375 | 4.7427 | | ddaa7a7249345a61a4ef3206c68fefae |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2232 - Accuracy: 0.9215 - F1: 0.9218 | 01cf06fcdf35d5f8352b24f0a3877412 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8098 | 1.0 | 250 | 0.3138 | 0.9025 | 0.9001 | | 0.2429 | 2.0 | 500 | 0.2232 | 0.9215 | 0.9218 | | 6f252d64376ce59d8b826a58d80167f3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 0.5730 | 0.7840 | | 86a78ba74aeeb83b12633f6b2ea24f51 |
creativeml-openrail-m | ['text-to-image'] | false | SksUminaoshiSimabu Dreambooth model trained by Hirokusa with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: ダウンロード .png) | ce2da44e00f43313b5917931c87342e9 |
mit | ['pytorch', 'diffusers', 'unconditional-image-generation', 'diffusion-models-class'] | false | Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. In this run I just ran each cell of the NB to understand what is going on. Experimentation to follow 🙏 | 0d0270bf23ae06d7c6ad38e95cb8c5d3 |
apache-2.0 | ['generated_from_trainer'] | false | mdeberta-targin-final This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5637 - Accuracy: 0.7091 - Precision: 0.6841 - Recall: 0.6557 - F1: 0.6617 | 336359cb9473ce866eac3df78b1c8697 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.6001 | 0.6435 | 0.6344 | 0.5087 | 0.4156 | | 0.6011 | 2.0 | 592 | 0.5633 | 0.7091 | 0.6879 | 0.6464 | 0.6521 | | 0.6011 | 3.0 | 888 | 0.5501 | 0.7234 | 0.6991 | 0.6841 | 0.6892 | | 0.5401 | 4.0 | 1184 | 0.5558 | 0.7082 | 0.6818 | 0.6595 | 0.6652 | | 0.5401 | 5.0 | 1480 | 0.5637 | 0.7091 | 0.6841 | 0.6557 | 0.6617 | | 06c4d63f61a350c2d5c52266f114081e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Telugu Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Telugu using the [OpenSLR SLR66](http://openslr.org/66/) dataset. When using this model, make sure that your speech input is sampled at 16kHz. | 72a22b81b24341099195b2ac7c8b441b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import pandas as pd | 5808b28679e656984b7f78bd3ddae7c0 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation notebook contains the procedure to download the data df = pd.read_csv("/content/te/test.tsv", sep="\t") df["path"] = "/content/te/clips/" + df["path"] test_dataset = Dataset.from_pandas(df) processor = Wav2Vec2Processor.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu") model = Wav2Vec2ForCTC.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu") resampler = torchaudio.transforms.Resample(48_000, 16_000) | db710b7fc05409a6d1250ab4336bfef1 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation ```python import torch import torchaudio from datasets import Dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re from sklearn.model_selection import train_test_split import pandas as pd | 187c3acea47e5c6b76b7159a8b6f72a4 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation notebook contains the procedure to download the data df = pd.read_csv("/content/te/test.tsv", sep="\t") df["path"] = "/content/te/clips/" + df["path"] test_dataset = Dataset.from_pandas(df) wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu") model = Wav2Vec2ForCTC.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\_\;\:\"\“\%\‘\”\।\’\'\&]' resampler = torchaudio.transforms.Resample(48_000, 16_000) def normalizer(text): | a73f51f624cf21d6bf6242bff208968a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Use your custom normalizer text = text.replace("\\n","\n") text = ' '.join(text.split()) text = re.sub(r'''([a-z]+)''','',text,flags=re.IGNORECASE) text = re.sub(r'''%'''," శాతం ", text) text = re.sub(r'''(/|-|_)'''," ", text) text = re.sub("ై","ై", text) text = text.strip() return text def speech_file_to_array_fn(batch): batch["sentence"] = normalizer(batch["sentence"]) batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()+ " " speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) | a961eee82f284eceb106ac26f6ac981d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 44.98% | fe421ae70d2a3455b87a0b88104f4649 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training 70% of the OpenSLR Telugu dataset was used for training. Train Split of annotations is [here](https://www.dropbox.com/s/xqc0wtour7f9h4c/train.tsv) Test Split of annotations is [here](https://www.dropbox.com/s/qw1uy63oj4qdiu4/test.tsv) Training Data Preparation notebook can be found [here](https://colab.research.google.com/drive/1_VR1QtY9qoiabyXBdJcOI29-xIKGdIzU?usp=sharing) Training notebook can be found[here](https://colab.research.google.com/drive/14N-j4m0Ng_oktPEBN5wiUhDDbyrKYt8I?usp=sharing) Evaluation notebook is [here](https://colab.research.google.com/drive/1SLEvbTWBwecIRTNqpQ0fFTqmr1-7MnSI?usp=sharing) | 47e124b603e8a46f27f7b3eee38ae55c |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | DreamBooth model for the stupa concept trained by Someman on the Someman/boudhastupa dataset. This is a Stable Diffusion model fine-tuned on the concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of boudhanath stupa** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! | 2b360235c820f80b93b06bd4cb6b381a |
cc-by-4.0 | ['generated_from_trainer'] | false | bert-base-cased-squad2-coffee20230113 This model is a fine-tuned version of [deepset/bert-base-cased-squad2](https://huggingface.co/deepset/bert-base-cased-squad2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.3859 | 45905d8a96318d3e062ddbc7306b9002 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 88 | 2.0785 | | 2.2589 | 2.0 | 176 | 1.9542 | | 1.4038 | 3.0 | 264 | 1.7714 | | 0.9533 | 4.0 | 352 | 2.2673 | | 0.5394 | 5.0 | 440 | 2.5496 | | 0.4353 | 6.0 | 528 | 3.2302 | | 0.4201 | 7.0 | 616 | 3.7247 | | 0.2477 | 8.0 | 704 | 3.4248 | | 0.2477 | 9.0 | 792 | 3.8344 | | 0.1633 | 10.0 | 880 | 4.1582 | | 0.0979 | 11.0 | 968 | 3.8764 | | 0.0621 | 12.0 | 1056 | 4.1686 | | 0.0242 | 13.0 | 1144 | 4.2762 | | 0.0091 | 14.0 | 1232 | 4.4176 | | 0.0061 | 15.0 | 1320 | 4.3859 | | 4df6cade815d2752b4f2d6fd52f88cf9 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_2000k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 3, Step 2000k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model | ae3c14cd85fc991487b12ccf3d13db28 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_2000k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_2000k') model = TFBertModel.from_pretrained("google/multiberts-seed_3-step_2000k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_2000k') model = BertModel.from_pretrained("google/multiberts-seed_3-step_2000k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | a1803a30e7a8e154acd1e72066943a7b |
gpl-3.0 | ['audio', 'music'] | false | This model encodes audio files into vectors of 100 dimensions. It was trained on 240,000 Spotify playlists and on 30 second samples of over 4 million songs. The details can be found [here](https://github.com/teticio/Deej-AI). To encode an audio first install the package with ``` pip install audiodiffusion ``` and then run ```python from audiodiffusion.audio_encoder import AudioEncoder audio_encoder = AudioEncoder.from_pretrained("teticio/audio-encoder") audio_encoder.encode(<list of audio files>) ``` | 5a4c3f4092735b860afcb277e1e71e2b |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-0.0-0.5 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8994 - Bleu: 7.5838 - Gen Len: 45.058 | 5ff66ac0612d1b8be0e8ef212c2dd4b8 |
mit | ['torch'] | false | How to use Here is how to use this model in PyTorch: ```python >>> from transformers import EncoderDecoderModel, XLMRobertaTokenizer >>> >>> model_id = "rmihaylov/roberta2roberta-shared-nmt-bg" >>> model = EncoderDecoderModel.from_pretrained(model_id) >>> model.encoder.pooler = None >>> tokenizer = XLMRobertaTokenizer.from_pretrained(model_id) >>> >>> text = """ Others were photographed ransacking the building, smiling while posing with congressional items such as House Speaker Nancy Pelosi's lectern or at her staffer's desk, or publicly bragged about the crowd's violent and destructive joyride. """ >>> >>> inputs = tokenizer.encode_plus(text, max_length=100, return_tensors='pt', truncation=True) >>> >>> translation = model.generate(**inputs, >>> max_length=100, >>> num_beams=4, >>> do_sample=True, >>> num_return_sequences=1, >>> top_p=0.95, >>> decoder_start_token_id=tokenizer.bos_token_id) >>> >>> print([tokenizer.decode(g.tolist(), skip_special_tokens=True) for g in translation]) ['Други бяха заснети да бягат из сградата, усмихвайки се, докато се представят с конгресни предмети, като например лекцията на председателя на парламента Нанси Пелози или на бюрото на нейния служител, или публично се хвалят за насилието и разрушителната радост на тълпата.'] ``` | 3accb120bf654ddaabc8055a9ce6a585 |
mit | ['torch'] | false | How to use Here is how to use this model in PyTorch: ```python >>> from transformers import PegasusForConditionalGeneration, AutoTokenizer >>> >>> model_id = "rmihaylov/pegasus-base-cnn-dailymail-bg" >>> model = PegasusForConditionalGeneration.from_pretrained(model_id) >>> tokenizer = AutoTokenizer.from_pretrained(model_id) >>> >>> text = """Лукашенко поиска още полицията "да защити работническите колективи и организации и медии от заплахите на улицата", а който от държавните медии протестира, изобщо да не се връща на работа. На граничните служби бе наредено да засилят охраната на цялата граница, "за да не се допускат в Беларус от други държави бойци, оръжие, боеприпаси, пари за финансиране на безредиците, защото виждаме, че такива пари пристигат". Министерството на отбраната трябва да следи "движението на войски на НАТО на територията на Полша и Литва, тяхното направление и замисли, които в момента виждаме - и някои от тях ни карат да се замислим - и да не се притеснява да изкарва нашите въоръжени сили и техника в направлението на тяхното придвижване". Лукашенко изрично посочи събитията в град Гродно, "защото там има по-голямо желание за дестабилизация на обстановката, отколкото в Минск". Гродно стана вчера първият по-голям град, в който властите се разбраха с протестиращите да протестират на определени места в центъра на града. Той нарече опозицията "черносотници", тласкащи страната към пропаст и унищожение, както и към сблъсък с "исторически братския руски народ". Медиите трябва специално да се активизират срещу това, заръча Лукашенко.""" >>> >>> batch = tokenizer( >>> src_text, >>> truncation=True, >>> padding="longest", >>> return_tensors="pt", >>> return_token_type_ids=False) >>> >>> inputs = { >>> 'max_length': 150, >>> 'min_length': 10, >>> 'do_sample': False, >>> 'temperature': 1.0, >>> 'top_k': 50, >>> 'top_p': 1.0, >>> 'repetition_penalty': 1.0, >>> 'no_repeat_ngram_size': 0, >>> 'use_cache': True, >>> 'num_beams': 2, >>> 'length_penalty': 1.0, >>> 'num_return_sequences': 1, >>> 'early_stopping': False} >>> >>> batch.update(inputs) >>> >>> summary = model.generate(**batch) >>> >>> tgt_text = tokenizer.batch_decode(summary, skip_special_tokens=True) >>> print(tgt_text) ['Лукашенко изрично посочи събитията в Гродно, "защото там има по-голямо желание за дестабилизация на обстановката, отколкото в Минск" Той нарече опозицията "черносотници", тласкащи страната към пропаст и унищожение, както и сблъсък с "исторически братския руски народ"'] ``` | 107a2b605d610ffaf1473bbbc4659a3a |
mit | ['generated_from_trainer'] | false | kobart_4_5.6e-5_datav2_min30_lp5.0_temperature1.0 This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9891 - Rouge1: 35.4597 - Rouge2: 12.0824 - Rougel: 23.0161 - Bleu1: 29.793 - Bleu2: 16.882 - Bleu3: 9.6468 - Bleu4: 5.3654 - Gen Len: 50.6014 | 2477f6399900a357a2175bbbff8a3e9d |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 4 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 | 6e22a32ba6c049a45550a885560f288e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:-------:| | 2.3968 | 0.47 | 5000 | 2.9096 | 32.7469 | 10.9679 | 21.4954 | 27.0594 | 15.1133 | 8.4503 | 4.564 | 48.5501 | | 2.2338 | 0.94 | 10000 | 2.8002 | 33.2148 | 11.5121 | 22.7066 | 26.4886 | 15.0125 | 8.5792 | 4.8523 | 41.1049 | | 1.9652 | 1.42 | 15000 | 2.7699 | 34.4269 | 11.8551 | 22.8478 | 28.2628 | 16.0909 | 9.0427 | 4.9254 | 46.9744 | | 2.001 | 1.89 | 20000 | 2.7201 | 34.157 | 11.8683 | 22.6775 | 28.3593 | 16.1361 | 9.221 | 4.8616 | 46.979 | | 1.6433 | 2.36 | 25000 | 2.7901 | 33.6354 | 11.5761 | 22.6878 | 27.6475 | 15.6571 | 8.8372 | 4.8672 | 43.9953 | | 1.6204 | 2.83 | 30000 | 2.7724 | 34.9611 | 12.1606 | 23.0246 | 29.1014 | 16.6689 | 9.3661 | 5.1916 | 48.8811 | | 1.2955 | 3.3 | 35000 | 2.8970 | 35.896 | 12.7037 | 23.3781 | 29.9701 | 17.3963 | 10.2978 | 5.9339 | 49.5921 | | 1.3501 | 3.78 | 40000 | 2.8854 | 35.2981 | 12.1133 | 23.1845 | 29.483 | 16.7795 | 9.4124 | 5.2042 | 48.5897 | | 1.0865 | 4.25 | 45000 | 2.9912 | 35.581 | 12.5145 | 23.2262 | 29.9364 | 17.2064 | 10.0427 | 5.62 | 48.31 | | 1.052 | 4.72 | 50000 | 2.9891 | 35.4597 | 12.0824 | 23.0161 | 29.793 | 16.882 | 9.6468 | 5.3654 | 50.6014 | | 59fdcef48c83b7ee85d8c52f2cc4590a |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Epic Diffusion: Source(s): [Hugging Face](https://huggingface.co/johnslegers/epic-diffusion) - [CivitAI](https://civitai.com/models/3855/epic-diffusion) Why Epic Diffusion Epîc Diffusion is a general purpose model based on Stable Diffusion 1.x intended to replace the official SD releases as your default model. It is focused on providing high quality output in a wide range of different styles, with support for NFSW content. Epîc Diffusion 1.0 is a heavily calibrated merge of SD 1.4, SD 1.5, Analog Diffusion, Wavy Diffusion, Openjourney Diffusion, Samdoesarts Ultramerge, postapocalypse, Elldreth's Dream, Protogen V2.2, Inkpunk Diffusion, Arcane Diffusion & Van Gogh Diffusion blended and reblended multiple times until I got the quality & consistency I was looking for... | d825adc245724214697cc54b48bfcd20 |
afl-3.0 | [] | false | This model is actually very accurate for this rerank products given one query, intuitively inspired by information retrieval techniques. In 2019, Nils Reimers and Iryna Gurevych introduced a new transformers model called Sentence-BERT, Sentence Embeddings using Siamese BERT-Networks. The model is introduced by this paper https://doi.org/10.48550/arxiv.1908.10084. This new Sentence-BERT model is modified on the BERT model by adding a pooling operation to the output of BERT model. In such a way, it can output a fixed size of the sentence embedding to calculate cosine similarity, and so on. To obtain a meaningful sentence embedding in a sentence vector space where similar or pairwise sentence embedding are close, they created a triplet network to modify the BERT model as the architecture below figure.  | 0c7cd2e1dff8e0b5100f173af153853a |
afl-3.0 | [] | false | Download and Use ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LiYuan/Amazon-Cup-Cross-Encoder-Regression") model = AutoModelForSequenceClassification.from_pretrained("LiYuan/Amazon-Cup-Cross-Encoder-Regression") ``` As we can observe from above figure, a pooling layer is added on the top of each BERT Model to obtain the sentence embedding $u$ and $v$. Finally, the cosine similarity between $u$ and $v$ can be computed to compare with the true score or make them semantically meaningful, then the mean square error loss, which is the objective function, can be backpropagated through this BERT network model to update the weights. In our amazon case, the query is sentence A and concatenated product attributes are sentence B. We also stratified split the merged set into **571,223** rows for training, **500** rows for validation, **3,000** rows for test. We limited the output score between 0 and 1. The following scores represent the degree of relevance between the query and the product attributes in light of Amazon KDD Cup website; however, this can be adjusted to improve the model performance. - 1: exact - 0.1: substitute - 0.01: complement - 0: irrelevance For this regression model, we used Pearson correlation coefficient and Spearman's rank correlation coefficient} to measure the model performance. If the correlation coefficient is high, the model performs well. The validation Pearson is \textbf{0.5670} and validation Spearman is \textbf{0.5662}. This is not bad result. We also evaluated the model on the test set. We got **0.5321** for Pearson and **0.5276** for Spearman. These results from the test evaluation have results similar to those of the validation set, suggesting that the model has a good generalization. Finally, once we have this fine-tuned Cross-Encoder Regression model, given a new query and its matched product list, we can feed them into this model to get the output score to rerank them so that this can improve the customer online shopping experience. | caf331bce3240732225d2faef8a09f01 |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_hubert_s722 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (it)](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. | 921850125eb25f760976ea393633fa31 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset. It achieves the following results on the evaluation set: - Loss: 0.0639 - Wer: 0.0449 | 53c714c7985258635f2c9a1758ca1256 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.603 | 0.72 | 500 | 4.6572 | 0.9985 | | 2.6314 | 1.44 | 1000 | 2.0424 | 0.9256 | | 2.2708 | 2.16 | 1500 | 0.9889 | 0.6989 | | 2.1769 | 2.88 | 2000 | 0.8366 | 0.6312 | | 2.1142 | 3.6 | 2500 | 0.7555 | 0.5998 | | 2.0084 | 4.32 | 3000 | 0.7144 | 0.6003 | | 1.9272 | 5.04 | 3500 | 0.6311 | 0.5461 | | 1.8687 | 5.75 | 4000 | 0.6252 | 0.5430 | | 1.8186 | 6.47 | 4500 | 0.5491 | 0.4988 | | 1.7364 | 7.19 | 5000 | 0.5463 | 0.4959 | | 1.6809 | 7.91 | 5500 | 0.4724 | 0.4484 | | 1.641 | 8.63 | 6000 | 0.4679 | 0.4461 | | 1.572 | 9.35 | 6500 | 0.4387 | 0.4236 | | 1.5256 | 10.07 | 7000 | 0.3970 | 0.4003 | | 1.5044 | 10.79 | 7500 | 0.3690 | 0.3893 | | 1.4563 | 11.51 | 8000 | 0.3752 | 0.3875 | | 1.394 | 12.23 | 8500 | 0.3386 | 0.3567 | | 1.3641 | 12.95 | 9000 | 0.3290 | 0.3467 | | 1.2878 | 13.67 | 9500 | 0.2893 | 0.3135 | | 1.2602 | 14.39 | 10000 | 0.2723 | 0.3029 | | 1.2302 | 15.11 | 10500 | 0.2603 | 0.2989 | | 1.1865 | 15.83 | 11000 | 0.2440 | 0.2794 | | 1.1491 | 16.55 | 11500 | 0.2500 | 0.2788 | | 1.093 | 17.27 | 12000 | 0.2279 | 0.2629 | | 1.0367 | 17.98 | 12500 | 0.2076 | 0.2443 | | 0.9954 | 18.7 | 13000 | 0.1844 | 0.2259 | | 0.99 | 19.42 | 13500 | 0.1794 | 0.2179 | | 0.9385 | 20.14 | 14000 | 0.1765 | 0.2122 | | 0.8952 | 20.86 | 14500 | 0.1706 | 0.1974 | | 0.8841 | 21.58 | 15000 | 0.1791 | 0.1969 | | 0.847 | 22.3 | 15500 | 0.1780 | 0.2060 | | 0.8669 | 23.02 | 16000 | 0.1608 | 0.1862 | | 0.8066 | 23.74 | 16500 | 0.1447 | 0.1626 | | 0.7908 | 24.46 | 17000 | 0.1457 | 0.1655 | | 0.7459 | 25.18 | 17500 | 0.1350 | 0.1445 | | 0.7218 | 25.9 | 18000 | 0.1276 | 0.1421 | | 0.703 | 26.62 | 18500 | 0.1177 | 0.1302 | | 0.685 | 27.34 | 19000 | 0.1147 | 0.1305 | | 0.6811 | 28.06 | 19500 | 0.1128 | 0.1244 | | 0.6444 | 28.78 | 20000 | 0.1120 | 0.1213 | | 0.6323 | 29.5 | 20500 | 0.1137 | 0.1166 | | 0.5998 | 30.22 | 21000 | 0.1051 | 0.1107 | | 0.5706 | 30.93 | 21500 | 0.1035 | 0.1037 | | 0.5555 | 31.65 | 22000 | 0.1031 | 0.0927 | | 0.5389 | 32.37 | 22500 | 0.0997 | 0.0900 | | 0.5201 | 33.09 | 23000 | 0.0920 | 0.0912 | | 0.5146 | 33.81 | 23500 | 0.0929 | 0.0947 | | 0.515 | 34.53 | 24000 | 0.1000 | 0.0953 | | 0.4743 | 35.25 | 24500 | 0.0922 | 0.0892 | | 0.4707 | 35.97 | 25000 | 0.0852 | 0.0808 | | 0.4456 | 36.69 | 25500 | 0.0855 | 0.0779 | | 0.443 | 37.41 | 26000 | 0.0843 | 0.0738 | | 0.4388 | 38.13 | 26500 | 0.0816 | 0.0699 | | 0.4162 | 38.85 | 27000 | 0.0752 | 0.0645 | | 0.3979 | 39.57 | 27500 | 0.0761 | 0.0621 | | 0.3889 | 40.29 | 28000 | 0.0771 | 0.0625 | | 0.3923 | 41.01 | 28500 | 0.0755 | 0.0598 | | 0.3693 | 41.73 | 29000 | 0.0730 | 0.0578 | | 0.3642 | 42.45 | 29500 | 0.0739 | 0.0598 | | 0.3532 | 43.17 | 30000 | 0.0712 | 0.0553 | | 0.3513 | 43.88 | 30500 | 0.0762 | 0.0516 | | 0.3349 | 44.6 | 31000 | 0.0731 | 0.0504 | | 0.3305 | 45.32 | 31500 | 0.0725 | 0.0507 | | 0.3285 | 46.04 | 32000 | 0.0709 | 0.0489 | | 0.3179 | 46.76 | 32500 | 0.0667 | 0.0467 | | 0.3158 | 47.48 | 33000 | 0.0653 | 0.0494 | | 0.3033 | 48.2 | 33500 | 0.0638 | 0.0456 | | 0.3023 | 48.92 | 34000 | 0.0644 | 0.0464 | | 0.2975 | 49.64 | 34500 | 0.0643 | 0.0455 | | 43804daf83b54a1d00a04807e9cd37bc |
apache-2.0 | ['translation'] | false | opus-mt-es-cs * source languages: es * target languages: cs * OPUS readme: [es-cs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-cs/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/es-cs/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-cs/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-cs/opus-2020-01-16.eval.txt) | b83911c98602ebc3a04d51973a998484 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-wikihow_3epoch_v2 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset. It achieves the following results on the evaluation set: - Loss: 2.2758 - Rouge1: 27.48 - Rouge2: 10.7621 - Rougel: 23.4136 - Rougelsum: 26.7923 - Gen Len: 18.5424 | 02328779394a4d29c58639e3dda93d98 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.8423 | 0.13 | 5000 | 2.5715 | 25.2685 | 8.6964 | 21.229 | 24.5773 | 18.4479 | | 2.7345 | 0.25 | 10000 | 2.5236 | 24.982 | 8.7823 | 21.1609 | 24.3066 | 18.3631 | | 2.6811 | 0.38 | 15000 | 2.4911 | 25.7585 | 9.3372 | 21.8388 | 25.1052 | 18.3997 | | 2.6611 | 0.51 | 20000 | 2.4510 | 26.022 | 9.4708 | 22.0899 | 25.3236 | 18.5472 | | 2.6133 | 0.64 | 25000 | 2.4272 | 26.3481 | 9.6769 | 22.4484 | 25.7046 | 18.3863 | | 2.6083 | 0.76 | 30000 | 2.4108 | 26.4131 | 9.6643 | 22.4021 | 25.6958 | 18.5585 | | 2.5842 | 0.89 | 35000 | 2.3866 | 26.2852 | 9.7505 | 22.4525 | 25.5908 | 18.5485 | | 2.5554 | 1.02 | 40000 | 2.3816 | 26.3018 | 9.7218 | 22.3673 | 25.6515 | 18.4912 | | 2.4895 | 1.14 | 45000 | 2.3730 | 26.6439 | 9.9665 | 22.6593 | 25.9521 | 18.5635 | | 2.4781 | 1.27 | 50000 | 2.3541 | 26.8488 | 10.0364 | 22.8202 | 26.1598 | 18.4254 | | 2.4821 | 1.4 | 55000 | 2.3440 | 26.9511 | 10.2079 | 23.0133 | 26.2821 | 18.5712 | | 2.4593 | 1.53 | 60000 | 2.3370 | 26.945 | 10.3123 | 22.9245 | 26.2493 | 18.5978 | | 2.4521 | 1.65 | 65000 | 2.3309 | 26.9652 | 10.314 | 22.9657 | 26.298 | 18.4837 | | 2.4523 | 1.78 | 70000 | 2.3249 | 27.0548 | 10.4204 | 23.1286 | 26.379 | 18.4717 | | 2.4563 | 1.91 | 75000 | 2.3079 | 27.4563 | 10.6452 | 23.3985 | 26.7812 | 18.5642 | | 2.4229 | 2.03 | 80000 | 2.3115 | 27.0538 | 10.44 | 22.9957 | 26.349 | 18.5914 | | 2.3694 | 2.16 | 85000 | 2.3017 | 27.332 | 10.6556 | 23.3135 | 26.629 | 18.459 | | 2.3749 | 2.29 | 90000 | 2.2941 | 27.3294 | 10.5967 | 23.2039 | 26.6411 | 18.5179 | | 2.3779 | 2.42 | 95000 | 2.2891 | 27.3725 | 10.6539 | 23.3455 | 26.707 | 18.5367 | | 2.3638 | 2.54 | 100000 | 2.2895 | 27.3487 | 10.6738 | 23.2894 | 26.681 | 18.6128 | | 2.3549 | 2.67 | 105000 | 2.2833 | 27.408 | 10.6903 | 23.3575 | 26.7137 | 18.6035 | | 2.3652 | 2.8 | 110000 | 2.2788 | 27.561 | 10.8202 | 23.4672 | 26.8584 | 18.5565 | | 2.3553 | 2.93 | 115000 | 2.2758 | 27.48 | 10.7621 | 23.4136 | 26.7923 | 18.5424 | | 391ca9fcf950b57c7b4b9165555b54c1 |
apache-2.0 | ['generated_from_trainer'] | false | olm-bert-tiny-december-2022-target-glue-qqp This model is a fine-tuned version of [muhtasham/olm-bert-tiny-december-2022](https://huggingface.co/muhtasham/olm-bert-tiny-december-2022) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5217 - Accuracy: 0.7433 - F1: 0.6048 | 495a0515c52e8d4f8ef615cd46d488ca |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.6283 | 0.04 | 500 | 0.5955 | 0.6795 | 0.5186 | | 0.5875 | 0.09 | 1000 | 0.5763 | 0.6972 | 0.5596 | | 0.5791 | 0.13 | 1500 | 0.5690 | 0.6975 | 0.6011 | | 0.5666 | 0.18 | 2000 | 0.5536 | 0.7156 | 0.5520 | | 0.5568 | 0.22 | 2500 | 0.5447 | 0.7230 | 0.5709 | | 0.5489 | 0.26 | 3000 | 0.5386 | 0.7281 | 0.5665 | | 0.5465 | 0.31 | 3500 | 0.5305 | 0.7329 | 0.5917 | | 0.5384 | 0.35 | 4000 | 0.5262 | 0.7357 | 0.6231 | | 0.5422 | 0.4 | 4500 | 0.5207 | 0.7409 | 0.6200 | | 0.5299 | 0.44 | 5000 | 0.5217 | 0.7433 | 0.6048 | | 9d36768531ad96e2ef9815a7601e8b69 |
mit | ['bart'] | false | Model Description [**BART**](https://arxiv.org/pdf/1910.13461.pdf)(**B**idirectional and **A**uto-**R**egressive **T**ransformers)는 입력 텍스트 일부에 노이즈를 추가하여 이를 다시 원문으로 복구하는 `autoencoder`의 형태로 학습이 됩니다. 한국어 BART(이하 **KoBART**) 는 논문에서 사용된 `Text Infilling` 노이즈 함수를 사용하여 **40GB** 이상의 한국어 텍스트에 대해서 학습한 한국어 `encoder-decoder` 언어 모델입니다. 이를 통해 도출된 `KoBART-base`를 배포합니다. - **Developed by:** More information needed - **Shared by [Optional]:** Heewon(Haven) Jeon - **Model type:** Feature Extraction - **Language(s) (NLP):** Korean - **License:** MIT - **Parent Model:** BART - **Resources for more information:** - [GitHub Repo](https://github.com/haven-jeon/KoBART) - [Model Demo Space](https://huggingface.co/spaces/gogamza/kobart-summarization) | 43898082e8d5eb682223da09fcf85929 |
mit | ['bart'] | false | Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. | d5e927b10c2aa10428a6e82980bb1f4c |
mit | ['bart'] | false | of Sentences | |-------|---------------:| | Korean Wiki | 5M | | Other corpus | 0.27B | 한국어 위키 백과 이외, 뉴스, 책, [모두의 말뭉치 v1.0(대화, 뉴스, ...)](https://corpus.korean.go.kr/), [청와대 국민청원](https://github.com/akngs/petitions) 등의 다양한 데이터가 모델 학습에 사용되었습니다. `vocab` 사이즈는 30,000 이며 대화에 자주 쓰이는 아래와 같은 이모티콘, 이모지 등을 추가하여 해당 토큰의 인식 능력을 올렸습니다. > 😀, 😁, 😆, 😅, 🤣, .. , `:-)`, `:)`, `-)`, `(-:`... | 9b271b1630d1622034e48640f12f94bf |
mit | ['bart'] | false | of heads | ffn_dim | hidden_dims | |--------------|:----:|:-------:|--------:|--------:|--------:|--------------:| | `KoBART-base` | 124M | Encoder | 6 | 16 | 3072 | 768 | | | | Decoder | 6 | 16 | 3072 | 768 | | d529e9711364a6f6f75261afed92bf69 |
mit | ['bart'] | false | Results NSMC - acc. : 0.901 The model authors also note in the [GitHub Repo](https://github.com/haven-jeon/KoBART): | | [NSMC](https://github.com/e9t/nsmc)(acc) | [KorSTS](https://github.com/kakaobrain/KorNLUDatasets)(spearman) | [Question Pair](https://github.com/aisolab/nlp_classification/tree/master/BERT_pairwise_text_classification/qpair)(acc) | |---|---|---|---| | **KoBART-base** | 90.24 | 81.66 | 94.34 | | 13588b6d9367bca2ad086eac9a206f8a |
mit | ['bart'] | false | How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import PreTrainedTokenizerFast, BartModel tokenizer = PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-base-v2') model = BartModel.from_pretrained('gogamza/kobart-base-v2') ``` </details> | 69b66baaf98a0e0de2d3d428944b8138 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_wnli_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 1.7589 - Accuracy: 0.1268 | 61b0cf7be740c23579d2f9bee2edbcd2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:----:|:--------:|:---------------:| | 0.6429 | 1.0 | 218 | 0.1268 | 1.7589 | | 0.4693 | 2.0 | 436 | 3.1597 | 0.1127 | | 0.3905 | 3.0 | 654 | 4.0613 | 0.0704 | | 0.3365 | 4.0 | 872 | 4.4630 | 0.0986 | | 0.295 | 5.0 | 1090 | 5.3692 | 0.0845 | | 0.2593 | 6.0 | 1308 | 5.3990 | 0.0845 | | 3b842d26b5d90fa127fcdcd778ac411e |
apache-2.0 | ['automatic-speech-recognition', 'collectivat/tv3_parla', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'projecte-aina/parlament_parla', 'robust-speech-event'] | false | wav2vec2-xls-r-300m-ca-lm This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA, the [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla) and [parlament_parla](https://huggingface.co/datasets/projecte-aina/parlament_parla) datasets. It achieves the following results on the evaluation set (for the three datasets and without the LM): - Loss: 0.2472 - Wer: 0.1499 | 4804998615b0b0b04305fc63b374198f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 | 86407266f2ba34af43a95a3a00e37a62 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.5695 | 0.39 | 500 | 0.0591 | | 0.0606 | 0.77 | 1000 | 0.0588 | | 0.0575 | 1.16 | 1500 | 0.0588 | | 0.0551 | 1.55 | 2000 | 0.0586 | | 0.0549 | 1.93 | 2500 | 0.0581 | | 0.0487 | 2.32 | 3000 | 0.0597 | | 0.0478 | 2.71 | 3500 | 0.0594 | | 0.0463 | 3.1 | 4000 | 0.0624 | | 0.0404 | 3.48 | 4500 | 0.0625 | | 0.041 | 3.87 | 5000 | 0.0617 | | 0.0366 | 4.26 | 5500 | 0.0656 | | 0.0347 | 4.64 | 6000 | 0.0658 | | 618f3406f4954b946abc5c143b754eb0 |
apache-2.0 | ['generated_from_trainer'] | false | test This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2778 - Accuracy: 0.9335 - F1:: 0.9337 | 11f8e6ee1404ff79a58b8921bbab9e21 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1: | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3285 | 0.9285 | 0.9291 | | No log | 2.0 | 500 | 0.2778 | 0.9335 | 0.9337 | | 46178b6329e9b3440db8b4793902540f |
mit | [] | false | PolicyBERTa-7d This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on data from the [Manifesto Project](https://manifesto-project.wzb.eu/). It was inspired by the model from [Laurer (2020)](https://huggingface.co/MoritzLaurer/policy-distilbert-7d). It achieves the following results on the evaluation set: - Loss: 0.8549 - Accuracy: 0.7059 - F1-micro: 0.7059 - F1-macro: 0.6683 - F1-weighted: 0.7033 - Precision: 0.7059 - Recall: 0.7059 | 36816339ad3ee2512c64355292c6e000 |
mit | [] | false | Model description This model was trained on 115,943 manually annotated sentences to classify text into one of seven political categories: "external relations", "freedom and democracy", "political system", "economy", "welfare and quality of life", "fabric of society" and "social groups". | 9dc22d6c03f4ffcfb3870e62b6425508 |
mit | [] | false | Intended uses & limitations The model output reproduces the limitations of the dataset in terms of country coverage, time span, domain definitions and potential biases of the annotators - as any supervised machine learning model would. Applying the model to other types of data (other types of texts, countries etc.) will reduce performance. ```python from transformers import pipeline import pandas as pd classifier = pipeline( task="text-classification", model="niksmer/PolicyBERTa-7d") | 753aa62920ea3b8d5ce1025949a172e9 |
mit | [] | false | Training and evaluation data PolicyBERTa-7d was trained on the English-speaking subset of the [Manifesto Project Dataset (MPDS2021a)](https://manifesto-project.wzb.eu/datasets). The model was trained on 115,943 sentences from 163 political manifestos in 7 English-speaking countries (Australia, Canada, Ireland, New Zealand, South Africa, United Kingdom, United States). The manifestos were published between 1992 - 2020. | Country | Count manifestos | Count sentences | Time span | |----------------|------------------|-----------------|--------------------| | Australia | 18 | 14,887 | 2010-2016 | | Ireland | 23 | 24,966 | 2007-2016 | | Canada | 14 | 12,344 | 2004-2008 & 2015 | | New Zealand | 46 | 35,079 | 1993-2017 | | South Africa | 29 | 13,334 | 1994-2019 | | USA | 9 | 13,188 | 1992 & 2004-2020 | | United Kingdom | 34 | 30,936 | 1997-2019 | Canadian manifestos between 2004 and 2008 are used as test data. The Manifesto Project mannually annotates individual sentences from political party manifestos in 7 main political domains: 'Economy', 'External Relations', 'Fabric of Society', 'Freedom and Democracy', 'Political System', 'Welfare and Quality of Life' or 'Social Groups' - see the [codebook](https://manifesto-project.wzb.eu/down/papers/handbook_2021_version_5.pdf) for the exact definitions of each domain. | 636e5ef480aca2e15a32d083895e5628 |
mit | [] | false | Tain data Train data was higly imbalanced. | Label | Description | Count | |------------|--------------|--------| | 0 | external relations | 7,640 | | 1 | freedom and democracy | 5,880 | | 2 | political system | 11,234 | | 3 | economy | 29,218 | | 4 | welfare and quality of life | 37,200 | | 5 | fabric of society | 13,594 | | 6 | social groups | 11,177 | Overall count: 115,943 | db4ae762cfae63277a1b7986799a4927 |
mit | [] | false | Validation data The validation was created by chance. | Label | Description | Count | |------------|--------------|--------| | 0 | external relations | 1,345 | | 1 | freedom and democracy | 1,043 | | 2 | political system | 2,038 | | 3 | economy | 5,140 | | 4 | welfare and quality of life | 6,554 | | 5 | fabric of society | 2,384 | | 6 | social groups | 1,957 | Overall count: 20,461 | be5c21b003eec250543e66a1f0eb2903 |
mit | [] | false | Test data The test dataset contains ten canadian manifestos between 2004 and 2008. | Label | Description | Count | |------------|--------------|--------| | 0 | external relations | 824 | | 1 | freedom and democracy | 296 | | 2 | political system | 1,041 | | 3 | economy | 2,188 | | 4 | welfare and quality of life | 2,654 | | 5 | fabric of society | 940 | | 6 | social groups | 387 | Overall count: 8,330 | 7e4efdcca67c39d331f9a26c72cd060e |
mit | [] | false | Training hyperparameters The following hyperparameters were used during training: ``` training_args = TrainingArguments( warmup_steps=0, weight_decay=0.1, learning_rate=1e-05, fp16 = True, evaluation_strategy="epoch", num_train_epochs=5, per_device_train_batch_size=16, overwrite_output_dir=True, per_device_eval_batch_size=16, save_strategy="no", logging_dir='logs', logging_strategy= 'steps', logging_steps=10, push_to_hub=True, hub_strategy="end") ``` | de94d971a296d826fd4f0587a1b37fa7 |
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