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 | [] | false | Training data The following is the list of data sources. Total characters is about 507M. | Data | % | | ------------------------------------------------- | --: | | News Articles / Blogs | 58% | | Yue Wikipedia / EVCHK ... | d6829ac7f325bcdb933a97d16d8d0197 |
apache-2.0 | [] | false | Training procedure Model was trained on a single TPUv3 from the official repo with the default parameters. | Parameter | Value | | ------------------------------------------------ | ----: | | Batch Size | 256 | | Max Sequence Size ... | 724c7d664bb6f021740d78e9b77965c7 |
apache-2.0 | [] | false | Eval results Average evaluation task results over 10 runs. Comparison using the original repo model and code. Chinese models are available from [Joint Laboratory of HIT and iFLYTEK Research (HFL)](https://huggingface.co/hfl) | Model | DRCD (EM/F1) | openrice-senti | lihkg-cat | wordshk-sem | |:-----------:|:--... | cd352d7363f2f2cd0ec061c9e6135e0f |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-06 - train_batch_size: 64 - eval_batch_size: 4 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gam... | 548b7a96ca8e64446c460d6240ce98c5 |
apache-2.0 | ['summarization', 'spanish', 'encoder-decoder', 'beto'] | false | Hyperparameters { "dataset_config": "es", "dataset_name": "mlsum", "do_eval": true, "do_predict": true, "do_train": true, "fp16": true, "max_target_length": 64, "num_train_epochs": 10, "per_device_eval_batch_size": 4, "per_device_train_batch_size": 4, "predict_with_gener... | 03818383b7f371f0f278b1da710178de |
apache-2.0 | ['summarization', 'spanish', 'encoder-decoder', 'beto'] | false | Results | metric | score | | --- | ----- | | validation_loss | 2.5021677017211914 | | validation_rouge1 | 26.1256 | | validation_rouge2 | 9.2552 | | validation_rougeL | 21.4899 | | validation_rougeLsum | 21.8194 | | test_loss | 2.57672381401062 | | test_rouge1 | 25.8639 | | test_rouge2 | 8.911 | | test_rougeL | 21.242... | d77d4fb61eda6cd21a882f3d60e53b64 |
mit | [] | false | tubby on Stable Diffusion This is the `<tubby>` 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 you... | b3eb05bdc197361045b0091cf05cff27 |
other | ['stable-diffusion', 'text-to-image'] | false | ◆About - This model is designed with "anime-style" and "cute" in mind. - Realistic models are less assertive. - Sampler: DDIM or DPM++ SDE Karras - Steps: 20~ - Clipskip: 2 - CFG Scale: 5-12 - Denoise strength: 0.5-0.7(As you like) - Negative prompts are recommended for "7th_Layer". - vae: As you wish. (Any etc. If n... | acf4fd36103fbee04567cacf91e87945 |
other | ['stable-diffusion', 'text-to-image'] | false | ◆Colab Note [](https://colab.research.google.com/drive/1Blvf6pxo3dyh94BJ3zkKuti9EqKr6gCA?usp=share_link) - (I have not checked the operation but it probably works.) ---- | 63b23b03c3761d529e33079a46c6be6d |
other | ['stable-diffusion', 'text-to-image'] | false | ◆Comparison <img src="https://i.imgur.com/ryxZy77.png" width="1700" height=""> <img src="https://i.imgur.com/RG9dBLK.png" width="1700" height=""> ``` (masterpiece:1.2), (best quality:1.2), (((kawaii))), smile, cowboy shot, (delicate sunlight composition) 8, downtown, 1girl, solo, looking at viewer, full body, (silve... | b32dba8d70179f9f900e332f99d14011 |
other | ['stable-diffusion', 'text-to-image'] | false | ◆Sampler & CFG Scale <img src="https://i.imgur.com/yVeYk9f.jpg" width="1700" height=""> <img src="https://i.imgur.com/jneg6gY.jpg" width="1700" height=""> ``` (masterpiece:1.2), (best quality:1.2), kawaii, winter, ((street)), ((building)), (noon), 1girl, solo, looking at viewer, ((maid uniform)), (twintails), long h... | 56ce8a01f6b2859b6e55be2ce139929a |
apache-2.0 | ['generated_from_trainer'] | false | BART_reddit_other This model is a fine-tuned version of [sshleifer/distilbart-xsum-6-6](https://huggingface.co/sshleifer/distilbart-xsum-6-6) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.5792 - Rouge1: 18.5705 - Rouge2: 5.0107 - Rougel: 15.2581 - Rougelsum: 16.082 - Gen Len:... | f30ec9223d3dcffa4c12ebef9b6891b4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 3.7887 | 1.0 | 1875 | 3.6044 | 18.4668 | 5.182 | 15.359 | 16.169 | 19.34... | 2cb2998619e6f263c24c0a5724893722 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr 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: 0.1651 - F1: 0.8578 | 7a5b34f8d9a96a9633975d0eac1ba397 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.211 | 1.0 | 715 | 0.1834 | 0.8266 | | 0.1447 | 2.0 | 1430 | 0.1624 | 0.8464 | | 0.0933 | 3.0 | 2145 | 0.1651 | 0.8578 | ... | 6468028187639e461263c3906b18a9bd |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-from-scratch-custom-tokenizer-expand-vocab-target-glue-qnli This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola-from-scratch-custom-tokenizer-expand-vocab](https://huggingface.co/muhtasham/tiny-mlm-glue-cola-from-scratch-custom-tokenizer-expand-vocab) on the None dataset. It achieve... | 68f97ed99899a8b171129297582a11aa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6936 | 0.15 | 500 | 0.6930 | 0.5444 | | 0.6928 | 0.31 | 1000 | 0.6893 | 0.5737 | | 0.6786 | 0.46 | 1500 | 0.6640 | 0.... | 29d58d3e2d8084e9e55210d065ab6e0c |
apache-2.0 | [] | false | bert-base-lt-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the sam... | 5142234998f98f3bd51e9ff94591f10d |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-lt-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-lt-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https:/... | 6780ed820468e01e7f76c82f7238e20a |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-rte This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.6540 - Accuracy: 0.6065 | f13f178be0f1a1d33f98d7ae66e56b5a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7009 | 1.0 | 20 | 0.6781 | 0.5560 | | 0.6393 | 2.0 | 40 | 0.6540 | 0.6065 | | 0.4606 | 3.0 | 60 | 0.7134 | 0.... | 421395e21a1fe247fe88011e8c4eb05a |
mit | ['spacy', 'token-classification'] | false | Model description **uk_ner_web_trf_base** is a fine-tuned [XLM-Roberta model](https://huggingface.co/xlm-roberta-base) that is ready to use for **Named Entity Recognition** and achieves a performance close to **SoA** for the NER task for Ukrainian language. It has been trained to recognize four types of entities: loc... | ef4bbec39c584e4506bb93c7fcc9c42c |
apache-2.0 | ['automatic-speech-recognition', 'sv-SE'] | false | exp_w2v2t_sv-se_hubert_s805 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 (sv-SE)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech in... | 58daae6c74744d1114c24c136b8edd10 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-qqp-target-glue-mnli This model is a fine-tuned version of [muhtasham/small-mlm-glue-qqp](https://huggingface.co/muhtasham/small-mlm-glue-qqp) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6551 - Accuracy: 0.7219 | 61c4b4a940459181bc2fccfdb99a6bc0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9185 | 0.04 | 500 | 0.8285 | 0.6395 | | 0.8182 | 0.08 | 1000 | 0.7859 | 0.6628 | | 0.7779 | 0.12 | 1500 | 0.7475 | 0.... | 5b466bfd1748df2ee3f46c65cdf9fda6 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-issues-128 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: 1.2314 | 7bbb6001182a06b4ecdfa82f07d690e2 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 | 47e73d1a1460e04210a8b396e355cbff |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.193 | 1.0 | 146 | 1.7004 | | 1.7081 | 2.0 | 292 | 1.4895 | | 1.5458 | 3.0 | 438 | 1.4427 | | 1.4715 | 4.0 | 584 | 1.4081 ... | 4fc78a45c3a85e15fcd9739fbd11f55d |
apache-2.0 | ['science', 'multi-displinary'] | false | ScholarBERT_10_WB Model This is the **ScholarBERT_10_WB** variant of the ScholarBERT model family. The model is pretrained on a large collection of scientific research articles (**22.1B tokens**). Additionally, the pretraining data also includes the Wikipedia+BookCorpus, which are used to pretrain the [BERT-base](ht... | 34d30334e3542afbe5e70e90545616d8 |
apache-2.0 | ['science', 'multi-displinary'] | false | Training Dataset The vocab and the model are pertrained on **10% of the PRD** scientific literature dataset and Wikipedia+BookCorpus. The PRD dataset is provided by Public.Resource.Org, Inc. (“Public Resource”), a nonprofit organization based in California. This dataset was constructed from a corpus of journal art... | fcd6a737f4453e48b300233b036b9289 |
other | ['text-generation', 'opt'] | false | How to use You can use this model directly with a pipeline for text generation. ```python >>> from transformers import pipeline >>> generator = pipeline('text-generation', model="facebook/opt-iml-max-1.3b") >>> generator("What is the capital of USA?") ``` | 95e1b47bfe8255daa724f03f4c660b3c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__sst2__train-16-2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6748 - Accuracy: 0.6315 | 419a416363effe8fce3bb4a71a4526b7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7043 | 1.0 | 7 | 0.7054 | 0.2857 | | 0.6711 | 2.0 | 14 | 0.7208 | 0.2857 | | 0.6311 | 3.0 | 21 | 0.7365 | 0.... | 2d4edc138110b8e9e722175494e6c4a4 |
apache-2.0 | ['generated_from_trainer'] | false | bert-zs-sentence-classifier This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3663 - F1: 0.8483 | f461e88f3086838623ca9e73679ea053 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | 893f58e916632e77c6fc39c290b57eed |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.5973 | 0.01 | 500 | 0.5186 | 0.7538 | | 0.5021 | 0.03 | 1000 | 0.4646 | 0.7996 | | 0.4741 | 0.04 | 1500 | 0.4634 | 0.806... | 57497ae48a3ba5b4bc196734bebe39ba |
apache-2.0 | [] | false | bert-base-da-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the sam... | 71d97d25be7c08ae360095b58eabd18d |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-da-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-da-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https:/... | 922609467f5f4aa39814d44ff698c33a |
['cc0-1.0'] | ['reinforcement learning', 'proximal policy optimization'] | false | Keras Implementation of Proximal Policy Optimization on Cartpole Environment 🔨🤖 This repo contains the model and the notebook [to this Keras example on PPO for Cartpole](https://keras.io/examples/rl/ppo_cartpole/). Full credits to: Ilias Chrysovergis  | 78cee1bc180e3f73a18a9bd536b31d0a |
['cc0-1.0'] | ['reinforcement learning', 'proximal policy optimization'] | false | CartPole-v0 A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The system is controlled by applying a force of +1 or -1 to the cart. The pendulum starts upright, and the goal is to prevent it from falling over. A reward of +1 is provided for every timestep that the pole remai... | 100ad5cd8031e2255181c759c0d2923c |
['cc0-1.0'] | ['reinforcement learning', 'proximal policy optimization'] | false | Proximal Policy Optimization PPO is a policy gradient method and can be used for environments with either discrete or continuous action spaces. It trains a stochastic policy in an on-policy way. Also, it utilizes the actor critic method. The actor maps the observation to an action and the critic gives an expectation o... | 3783ca4f8e67c38a54d0bbafe67899df |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | DataikuNLP/distiluse-base-multilingual-cased-v1 **This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1) from sentence-transformers at the specific commit `3a706e4d65c04f868c4684adfd4da74141be8732`.** This is a [sentence-transformers](https:... | 7662d50fc36ead74806719bbc0fe57a3 |
other | ['generated_from_trainer'] | false | mit-b2-fv-finetuned-memes This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5984 - Accuracy: 0.8323 - Precision: 0.8312 - Recall: 0.8323 - F1: 0.8315 | a483a916c14f7d7fff82bb15b4d8ad67 |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.3683 | 0.99 | 20 | 1.1798 | 0.5703 | 0.4914 | 0.5703 | 0.4915 | | 1.0113 | 1.99 |... | c3d7a76b4a8a2c50c5973f6c31527420 |
apache-2.0 | [] | false | distilbert-base-en-pt-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accur... | a8f9272d961211223b00bb19229427fd |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-pt-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-pt-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Gi... | 1c509d5b70ef22a61644d73124d1ce3b |
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch'] | false | [](https://www.inesc-id.pt/projects/PR07005/) [](https://rufimelo99.github.io/SemanticSearchSystemFo... | 0a933db533bcf7a99e9667e49cff5fe1 |
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch'] | false | stjiris/bert-large-portuguese-cased-legal-tsdae (Legal BERTimbau) This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. stjiris/bert-large-portuguese-cased-legal-tsdae derives... | 48bf9b5f8ab92f739702bdfc2b0e41a2 |
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["Isto é um exemplo", "Isto ... | 906155512bc20f0c8c16b3cdbc5410e1 |
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('stjiris/bert-large-portuguese-cased-legal-tsdae') model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-tsdae') | 8f0ada0f4c88dbfa1c941ef4cafcd6d6 |
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1028, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_me... | 07585518c1687589ffd7554f8483e2b2 |
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch'] | false | Contributions [@rufimelo99](https://github.com/rufimelo99) If you use this work, please cite: ```bibtex @inproceedings{MeloSemantic, author = {Melo, Rui and Santos, Professor Pedro Alexandre and Dias, Professor Jo{\~ a}o}, title = {A {Semantic} {Search} {System} for {Supremo} {Tribunal} de {Justi}{\c c}a}, } @inp... | 8c82dbfda6fdfd94c2c5d1c3683c40cc |
mit | ['generated_from_trainer'] | false | gpt2.CEBaB_confounding.price_food_ambiance_negative.absa.5-class.seed_42 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the OpenTable OPENTABLE-ABSA dataset. It achieves the following results on the evaluation set: - Loss: 0.4726 - Accuracy: 0.8311 - Macro-f1: 0.8295 - Weighted-macro-f1:... | 8386855da210f8a7c96a7f8716d48f59 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab2 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: 1.2355 - Wer: 0.7320 | ab11c75638f4e4528433342d9a7216fa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.851 | 13.89 | 500 | 3.1260 | 1.0 | | 1.9721 | 27.78 | 1000 | 1.2435 | 0.7992 | | 0.5749 | 41.67 | 1500 | 1.1662 | 0.7374 | |... | 4f906f80fa54ae2508a1481bd9b186fd |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3717 - Wer: 0.2972 | b09b8f4d773072cf8477926ec51fda98 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.0139 | 3.67 | 400 | 0.7020 | 0.7112 | | 0.4129 | 7.34 | 800 | 0.4162 | 0.4503 | | 0.1869 | 11.01 | 1200 | 0.4174 | 0.3959 | |... | 76e46eef9947028fc01ec846385b2a48 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-all 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: 0.1917 - F1: 0.8522 | d6c457e90a7bada8b9d3beffaf17f4d8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 1251 | 0.2072 | 0.8084 | | No log | 2.0 | 2502 | 0.1911 | 0.8350 | | No log | 3.0 | 3753 | 0.1917 | 0.8522 | ... | ca40865cc7977a8ca4f2892d91b06f96 |
apache-2.0 | ['translation'] | false | jpn-tur * source group: Japanese * target group: Turkish * OPUS readme: [jpn-tur](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-tur/README.md) * model: transformer-align * source language(s): jpn jpn_Bopo jpn_Hang jpn_Hani jpn_Hira jpn_Kana jpn_Yiii * target language(s): tur * model: t... | 6f2ce4b4289838d771779db50ffa21fe |
apache-2.0 | ['translation'] | false | System Info: - hf_name: jpn-tur - source_languages: jpn - target_languages: tur - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-tur/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ja', 'tr'] - src_constituents: {'jpn_Hang', 'jpn', ... | a8a168a8ba179533aab78e759dd21fcc |
apache-2.0 | ['generated_from_trainer'] | false | qnli This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3731 - Accuracy: 0.9068 | 3ae8a111d175de9fb07e9809cb77a6a1 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 | 04d42f7718d61389adb7ae5f364ec003 |
apache-2.0 | ['SongNet', 'pytorch', 'zh', 'Text2Text-Generation'] | false | SongNet for Chinese Couplet(songnet-base-chinese-couplet) Model SongNet中文对联仿写模型 `songnet-base-chinese-couplet` evaluate couplet test data： The overall performance of SongNet on couplet **test**: |input_text|predict| |:--- |:--- | |一句相思吟岁月，千杯美酒醉风情|一生只剩诗和酒，满腹无关雪与梅| 在Couplet测试集上生成结果满足字数相同、词性对齐、词面对齐、形似要求，针对性的SongNet网络... | c2ae1c3a1ba7fb75439bd595cbd62dc8 |
apache-2.0 | ['SongNet', 'pytorch', 'zh', 'Text2Text-Generation'] | false | Usage 本项目开源在文本生成项目：[textgen](https://github.com/shibing624/textgen)，可支持SongNet模型，通过如下命令调用： Install package: ```shell pip install -U textgen ``` ```python import sys sys.path.append('..') from textgen.language_modeling import SongNetModel model = SongNetModel(model_type='songnet', model_name='shibing624/songnet-b... | 77dad5e46aef412d9bbe055fd93d7959 |
apache-2.0 | ['SongNet', 'pytorch', 'zh', 'Text2Text-Generation'] | false | 中文对联数据集 - 数据：[对联github](https://github.com/wb14123/couplet-dataset)、[清洗过的对联github](https://github.com/v-zich/couplet-clean-dataset) - 相关内容 - [Huggingface](https://huggingface.co/) - [SongNet paper](https://aclanthology.org/2020.acl-main.68/) - [textgen](https://github.com/shibing624/textgen) 数据格式： ```tex... | e21d8ca510be8afdfff850538a43e801 |
apache-2.0 | ['generated_from_keras_callback'] | false | tmp6tsjsfbf This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0178 - Train Sparse Categorical Accuracy: 0.9962 - Epoch: 49 | 5e8a796849505dd3f705d24ba48f7ca0 |
apache-2.0 | ['generated_from_keras_callback'] | false | Model description This model classifies the title of a content (e.g., YouTube video, article, or podcast episode) into 1 of 8 subjects 0. art 1. personal development 2. world 3. health 4. science 5. business 6. humanities 7. technology. This model is used to support [Sanderling](https://sanderling.app) | 991407d78932b7fc6527a7ff53884d49 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 5e-06, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 | 96d0cf71aca213d5d723bb635eab0d15 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Sparse Categorical Accuracy | Epoch | |:----------:|:---------------------------------:|:-----:| | 1.8005 | 0.3956 | 0 | | 1.3302 | 0.5916 | 1 | | 0.8998 | 0.7575 | 2 | | 0.62... | f7afd97fedaabdb74e62075a7ae41044 |
apache-2.0 | ['translation'] | false | opus-mt-et-sv * source languages: et * target languages: sv * OPUS readme: [et-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/et-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 97263d820608f6a1f6e5fe27daacf3bc |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 08c6efbc6299c972301236625f9abafe087c9f9c pip install -e . cd egs2/swbd_da/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/akreal_swbd_da_hubert_conformer ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 8736e09993989f741c97ac5bebdb800c |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Thu Jan 20 19:31:21 CET 2022` - python version: `3.8.12 (default, Aug 30 2021, 00:00:00) [GCC 11.2.1 20210728 (Red Hat 11.2.1-1)]` - espnet version: `espnet 0.10.6a1` - pytorch version: `pytorch 1.10.1+cu113` - Git hash: `08c6efbc6299c972301236625f9abafe087c9f9c` - Commit date: `Tue Jan 4 13:4... | a791d3539883ea4165f287c2e36fbb5c |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.loss.ave/test_context3|2379|2379|66.3|33.7|0.0|0.0|33.7|33.7| |decode_asr_asr_model_valid.loss.ave/valid_context3|8116|8116|69.5|30.5|0.0|0.0|30.5|30.5| | bccefe4b50c27649e8f4c629efaa9def |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.loss.ave/test_context3|2379|19440|76.1|17.7|6.2|8.1|32.0|33.7| |decode_asr_asr_model_valid.loss.ave/valid_context3|8116|66353|79.5|16.1|4.4|8.0|28.5|30.5| | cc598bfb75ca190b5c0ef9ea494b44a9 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_hubert_context3.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_hubert_context3_raw_en_word_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend:... | a6dd32dacecff23885a79fc921b22310 |
apache-2.0 | ['translation'] | false | rus-ara * source group: Russian * target group: Arabic * OPUS readme: [rus-ara](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-ara/README.md) * model: transformer * source language(s): rus * target language(s): apc ara arz * model: transformer * pre-processing: normalization + SentenceP... | 6ed12a9dbef04aff34a8c424913f7506 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: rus-ara - source_languages: rus - target_languages: ara - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-ara/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ru', 'ar'] - src_constituents: {'rus'} - tgt_const... | 3442f8f1f7e0f2ae555e1434ea240400 |
apache-2.0 | ['generated_from_trainer'] | false | bart-paraphrase-v4-e1-feedback-feedback-e1 This model is a fine-tuned version of [theojolliffe/bart-paraphrase-v4-e1-feedback](https://huggingface.co/theojolliffe/bart-paraphrase-v4-e1-feedback) on the None dataset. | dc2fae12558fe456e3aa6c45200d1c11 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 34 | 2.9415 | 60.8992 | 38.9444 | 51.1386 | 52.0048 | 19... | 5b024c4e98d96dc9fce1aae727cdf543 |
apache-2.0 | [] | false | Important This model generates 16:9 higher-detail wallpaper images. You **cannot use 512x512**, you **must use a minimum of 1024x576 or higher**. High-res fix is reccomended if going higher but using 1024x576 as the basis. | caa7b3d6170616b3377e99e9e6759186 |
apache-2.0 | [] | false | Images made with this model These were generated with high-res fix to start at 1024x576 with a 1.25 or 25% increase resulting in 1280x720    | ba12b3e79dce286546fe42b4478b6a48 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Model Card of `lmqg/mbart-large-cc25-frquad-qg-ae` This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation and answer extraction jointly on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via ... | fc512e566bf68eeb9d6d7acc2137f17b |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | model prediction question_answer_pairs = model.generate_qa("Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.") ``` - With ... | 2ca976f0b9bbb65d3127a913156d5b9e |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | answer extraction answer = pipe("generate question: Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême Berger <hl> » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.") | 3dde43dcf1857a84019f4ef15d028856 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | question generation question = pipe("extract answers: Pourtant, la strophe spensérienne, utilisée cinq fois avant que ne commence le chœur, constitue en soi un vecteur dont les répétitions structurelles, selon Ricks, relèvent du pur lyrisme tout en constituant une menace potentielle. Après les huit sages pentamètres i... | b69de666a78275f4f23a4788e71c72f1 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-frquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json) | | Score | Type | Dataset ... | 03ac73791f08c9f222d7c2b582028f86 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_frquad - dataset_name: default - input_types: ['paragraph_answer', 'paragraph_sentence'] - output_types: ['question', 'answer'] - prefix_types: ['qg', 'ae'] - model: facebook/mbart-large-cc25 - max_leng... | 989f16c87ca628aadb46560a22b3f97b |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | evgengrcivface Dreambooth model trained by tftgregrge 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-st... | 1d68540a84498f6efee1d489e7412768 |
mit | ['roberta-base', 'roberta-base-epoch_7'] | false | RoBERTa, Intermediate Checkpoint - Epoch 7 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly i... | dbf712c89d96dc09f7cd2b566b51b5c2 |
mit | ['zero-shot-image-classification', 'clip'] | false | Model Description A series of CLIP [ConvNeXt-Large](https://arxiv.org/abs/2201.03545) (w/ extra text depth, vision MLP head) models trained on the LAION-2B (english) subset of [LAION-5B](https://arxiv.org/abs/2210.08402) using [OpenCLIP](https://github.com/mlfoundations/open_clip). The models utilize: * the [timm]... | 2354e957848f80cfbcc8c004cbf9742d |
mit | ['zero-shot-image-classification', 'clip'] | false | Training Procedure All 320x320 model fine-tunes were trained with a global batch size of 131072 for 10-16 checkpoint intervals of 203.7M samples for a total of ~2-3B samples seen over fine-tune. For 320x320 models, a slurm script w/ srun below was used on 64 8-GPU (A100 40GB) nodes (Stability). ``` /opt/slurm/sbin/... | 1a55d81eacb5a3a93afee2e9cb938c64 |
mit | ['zero-shot-image-classification', 'clip'] | false | Results The models achieve between 75.9 and 76.9 top-1 zero-shot accuracy on ImageNet-1k. Zero-shot curve of origina from-scratch 256x256 training:  An initial round of benchmarks have been performed on a wider range of datasets, to be viewable at https://github.com/LAION-AI/CLIP_be... | 6417e44811b270bf16e87eb2b2bd7235 |
afl-3.0 | [] | false | **Please use 'Bert' related tokenizer classes and 'Nezha' related model classes** [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204) Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.... | 6720a1ac21ed9c1567c425599c8b644d |
afl-3.0 | [] | false | Example Usage ``` from transformers import BertTokenizer, NezhaModel tokenizer = BertTokenizer.from_pretrained("sijunhe/nezha-large-wwm") model = NezhaModel.from_pretrained("sijunhe/nezha-large-wwm") text = "我爱北京天安门" encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 874c87f4de1c7f8c7ac5297801f421ff |
other | [] | false | Green Carpet Cleaning Garland http://garlandcarpetcleaner.com/ (972) 256-8544 One of methods we follow at cover cleaning is "Steam Cleaning Administration" that depends on utilizing minimal high temp water and more steam, centering steam - which infiltrating into profound on spots and stain to dissolve every one of the... | 95bf9666a0fa3abf61fcb30ed3a4fa14 |
apache-2.0 | [] | false | it5-efficient-small-lfqa It is a T5 ([IT5](https://huggingface.co/stefan-it/it5-efficient-small-el32)) efficient small model trained on a lfqa dataset. <p align="center"> <img src="https://www.marcorossiartecontemporanea.net/wp-content/uploads/2021/04/MARCTM0413-9CFBn1gs-scaled.jpg" width="400"> </br> Mirc... | 9cf30c5e029bfe81be2f548476263904 |
apache-2.0 | [] | false | Usage and Performance ```python import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("efederici/it5-efficient-small-lfqa") model = AutoModelForSeq2SeqLM.from_pretrained("efederici/it5-efficient-small-lfqa") query = "con chi si era messo in contatto elon... | 24138f4b4812e7355b0e733cad0f31eb |
apache-2.0 | [] | false | concatenated texts/document text doc = """ La notizia dell’acquisizione da parte di Elon Musk del 9,2 per cento delle azioni di Twitter e del suo successivo ingresso nel consiglio di amministrazione della società hanno attirato grandi attenzioni, non solo da parte degli analisti finanziari, ma anche di chi si occupa d... | 517f65f3670a4c974fbdcc45202f64c9 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2562 - F1: 0.8223 | dfafcef2411e9711dcc1ccb767b85bc8 |
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