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 | ['translation'] | false | opus-mt-es-st * source languages: es * target languages: st * OPUS readme: [es-st](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-st/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | c57a05e76044311858fd30aaa60b46cf |
apache-2.0 | ['generated_from_keras_callback'] | false | imdb_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4690 - Validation Loss: 0.2538 - Train Accuracy: 0.904 - Epoch: 0 | 58955563f8590cef11bb0582c0b545df |
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': 625, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_... | 8173c5c4005c40c9adc0887be4eb9197 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Poison Model Welcome to poison model. This model is intended to produce high-quality, highly detailed anime style with just a few prompts. Unlike other anime style models, it has a little realistic style(but not too much), especially in the character painting. It's finetuned from [anything model](https://huggingface... | 297d1a804f4e4d786ab09f5c0a221c77 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Usage ``` import torch from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler repo_id = "mrdabin/poison" pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16") pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) pipe = pipe.to("cuda") ... | 93ff5f649b0c6f7c0175671a4e3980c4 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-eli5 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eli5 dataset. It achieves the following results on the evaluation set: - Loss: 3.6813 - Rouge1: 13.044 - Rouge2: 1.9483 - Rougel: 10.5237 - Rougelsum: 11.8549 - Gen Len: 18.997 | 339f5642a28563d4d6df8777bb38432b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:-------:|:---------:|:-------:| | 3.8881 | 1.0 | 17040 | 3.6813 | 13.044 | 1.9483 | 10.5237 | 11.8549 | 18.99... | 10d7462064b52e9e26e40ea3b067869c |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-irish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.148 - Wer: 52.4 | 3bd57069aff029b5495d81c139db0a88 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.6516 | 12.12 | 400 | 1.2867 | 0.7653 | | 0.4188 | 24.24 | 800 | 1.1262 | 0.5509 | | 6cff526c60b01b171043fd5f5b872cac |
apache-2.0 | ['roberta', 'NLU', 'NLI', 'Chinese'] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | Roberta | 330M | 中文-自然语言推断 Chinese-NLI | | 07c807964ae9c93b91196ddb5f949e71 |
apache-2.0 | ['roberta', 'NLU', 'NLI', 'Chinese'] | false | 模型信息 Model Information 基于[chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)，我们在收集的4个中文领域的NLI（自然语言推理）数据集，总计1014787个样本上微调了一个NLI版本。 Based on [chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large), we fine-tuned an NLI version on 4 Chinese Natural... | ee3afe381c25ede89372ed1e228f387f |
apache-2.0 | ['roberta', 'NLU', 'NLI', 'Chinese'] | false | 下游效果 Performance | 模型 Model | cmnli | ocnli | snli | | :--------: | :-----: | :----: | :-----: | | Erlangshen-Roberta-110M-NLI | 80.83 | 78.56 | 88.01 | | Erlangshen-Roberta-330M-NLI | 82.25 | 79.82 | 88 | | Erlangshen-MegatronBert-1.3B-NLI | 84.52 | 84.17 ... | cf7d5c58c1d73bb11ed1da146c207385 |
apache-2.0 | ['roberta', 'NLU', 'NLI', 'Chinese'] | false | 使用 Usage ``` python from transformers import BertForSequenceClassification from transformers import BertTokenizer import torch tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-330M-NLI') model=BertForSequenceClassification.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-330M-NLI') texta='今天的饭不好吃'... | 422455cc075845197abcccc2bf00c004 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_mnli_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.8790 - Accuracy: 0.6030 | df1f34d90c155f387bdd364d170162cc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0008 | 1.0 | 3068 | 0.9490 | 0.5405 | | 0.9205 | 2.0 | 6136 | 0.9166 | 0.5675 | | 0.8928 | 3.0 | 9204 | 0.9022 ... | 9160bfeed4e1c50fe0b0802e5a2db339 |
mit | [] | false | Marbling art on Stable Diffusion This is the `<marbling-art>` 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 ... | 5a81e3f583f1f567d78a355a49205e38 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - 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 - num_epochs: 5 - mixed_precision_training: Native AMP | c31f8ec6c6edcc6a42af734c7d49625f |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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: 3.0 - mixed_precision_training: Native AMP | 6a7095b5853979217ddc8f0621c31d51 |
apache-2.0 | ['translation'] | false | opus-mt-it-es * source languages: it * target languages: es * OPUS readme: [it-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/it-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-26.zip](https://... | 890c6a71c20f5768e97b2082e203c3b4 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'cosmosx', 'dreambooth'] | false | Cosmosx Rendered: Steps: 35, Default Automatic1111 settings <img src="https://huggingface.co/OlafII/cosmosx/resolve/main/images/01178-703442978-cosmosx, dog.png" width="100%"/> <img src="https://huggingface.co/OlafII/cosmosx/resolve/main/images/01191-56691087-cosmosx, goddess.png" width="100%"/> <img src="https://h... | 3cc15af0fe7d68935d7094b74e960a90 |
apache-2.0 | ['generated_from_trainer'] | false | w2v2-libri 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: 1.5387 - Wer: 0.5380 | f1e883f43e2b35cb27a26763f06ae54b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1500 - training_steps: 2500 - mixed_precis... | 9c297fb7468b46383e49cd6b4292db91 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 8.8253 | 50.0 | 200 | 3.1879 | 1.0 | | 3.0174 | 100.0 | 400 | 2.9619 | 1.0 | | 2.8589 | 150.0 | 600 | 2.9499 | 1.0 | |... | 034b059464d8fe0f1173a5034fc0b295 |
apache-2.0 | ['generated_from_trainer'] | false | distilgpt2-finetuned-irll2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.1925 | af1d9b6ac5de9a184df7320e975a8e0f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 12 | 4.2919 | | No log | 2.0 | 24 | 4.2158 | | No log | 3.0 | 36 | 4.1925 | | feff0779082f8362371ad962653cb6a6 |
apache-2.0 | ['text', 'tokenizer', 'preprocessor', 'bert', 'tensorflow'] | false | Overview This SavedModel is a companion of [BERT models](https://tfhub.dev/google/collections/bert/1) to preprocess plain text inputs into the input format expected by BERT. **Check the model documentation** to find the correct preprocessing model for each particular BERT or other Transformer encoder model. BERT and... | 943f2a8aed85dade56f68eb6623dc20e |
apache-2.0 | ['text', 'tokenizer', 'preprocessor', 'bert', 'tensorflow'] | false | Prerequisites This SavedModel uses TensorFlow operations defined by the [TensorFlow Text](https://github.com/tensorflow/text) library. On [Google Colaboratory](https://colab.research.google.com/), it can be installed with ``` !pip install tensorflow_text import tensorflow_text as text | 271acf52464bbf9044cd161fbd45df9e |
apache-2.0 | ['text', 'tokenizer', 'preprocessor', 'bert', 'tensorflow'] | false | Using TF Hub and HF Hub ``` model_path = snapshot_download(repo_id="Dimitre/bert_en_cased_preprocess") preprocessor = KerasLayer(handle=model_path) text_input = tf.keras.layers.Input(shape=(), dtype=tf.string) encoder_inputs = preprocessor(text_input) ``` | b7b73c917adf3dfb57f2213d439ea4fc |
apache-2.0 | ['text', 'tokenizer', 'preprocessor', 'bert', 'tensorflow'] | false | Using [TF Hub fork](https://github.com/dimitreOliveira/hub) ``` preprocessor = pull_from_hub(repo_id="Dimitre/bert_en_cased_preprocess") text_input = tf.keras.layers.Input(shape=(), dtype=tf.string) encoder_inputs = preprocessor(text_input) ``` The resulting encoder inputs have `seq_length=128`. | 5177ef7bb952e9893d3ea3be3ceda834 |
apache-2.0 | ['text', 'tokenizer', 'preprocessor', 'bert', 'tensorflow'] | false | General usage For pairs of input segments, to control the `seq_length`, or to modify tokenized sequences before packing them into encoder inputs, the preprocessor can be called like this: ``` preprocessor = pull_from_hub(repo_id="Dimitre/bert_en_cased_preprocess") | 67e8e171410fe14f01997cc8b379108a |
apache-2.0 | ['text', 'tokenizer', 'preprocessor', 'bert', 'tensorflow'] | false | Optional argument. encoder_inputs = bert_pack_inputs(tokenized_inputs) ``` The call to `tokenize()` returns an int32 [RaggedTensor](https://www.tensorflow.org/guide/ragged_tensor) of shape `[batch_size, (words), (tokens_per_word)]`. Correspondingly, the call to `bert_pack_inputs()` accepts a RaggedTensor of shape `[b... | ed9c82c5ee6a336e45893074abaa2f25 |
apache-2.0 | ['text', 'tokenizer', 'preprocessor', 'bert', 'tensorflow'] | false | Output details The result of preprocessing is a batch of fixed-length input sequences for the Transformer encoder. An input sequence starts with one start-of-sequence token, followed by the tokenized segments, each terminated by one end-of-segment token. Remaining positions up to `seq_length`, if any, are filled up ... | 05b7b4a0e147e65fe54a468546fb7996 |
apache-2.0 | ['text', 'tokenizer', 'preprocessor', 'bert', 'tensorflow'] | false | Custom input packing and MLM support The function ```special_tokens_dict = preprocessor.tokenize.get_special_tokens_dict()``` returns a dict of scalar int32 Tensors that report the tokenizer's `"vocab_size"` as well as the ids of certain special tokens: `"padding_id"`, `"start_of_sequence_id"` (aka. [CLS]), `"end_of... | e0a4fad90ac3ba59265a6551fd54d1ff |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model Details Neural machine translation model for translating from German (de) to Spanish (es). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are or... | 466bdc0f61d2d47920eb7b003ea3ac49 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | How to Get Started With the Model A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Ich verstehe nicht, worüber ihr redet.", "Die Vögel singen in den Bäumen." ] model_name = "pytorch-models/opus-mt-tc-big-de-es" tokenizer = MarianTokenizer.from_pretrained... | d7c4c0f0c4b039b015bb9b9bf9cecad0 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Los pájaros cantan en los árboles. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-de-es") print(pipe("Ich verstehe nicht, worüber ihr redet.")) | b428b7887a4e74cd6f55f49d931d6842 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Training - **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) - **Pre-processing**: SentencePiece (spm32k,spm32k) - **Model Type:** transformer-big - **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-26.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-s... | 94871b773f6404f913b7b58aadb1d382 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-07-26.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-spa/opusTCv20210807_transformer-big_2022-07-26.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-07-26.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/de... | 742a5f012099573eb844cf21d1bbbf27 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | deu-spa | tatoeba-test-v2021-08-07 | 0.69105 | 50.8 | 10521 | 82570 | | deu-spa | flores101-devtest | 0.53208 | 24.9 | 1012 | 29199 | | deu-spa | newssyscomb2009 | 0.55547 | 28.3 | 502 | 12503 | | deu-spa | news-test2008 | 0.54400 | 26.6 | 2051 | 52586 |... | c630145f7ae00581869fceb10e75e0b8 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-3'] | false | MultiBERTs Seed 3 Checkpoint 1100k (uncased) Seed 3 intermediate checkpoint 1100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/g... | f9c8a188e06c3f0b6d56c9f736a578cc |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-3'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-3-1100k') model = BertModel.from_pretrained("multiberts-seed-3-1100k") text = "Replace me by any text you'd lik... | a4c722295d32cc106eb59e80c4e1f5cb |
apache-2.0 | ['generated_from_trainer'] | false | nbme-electra-large-discriminator This model is a fine-tuned version of [google/electra-large-discriminator](https://huggingface.co/google/electra-large-discriminator) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.1201 | 72e7366a09240479438eed8dd82c7abb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.1704 | 1.0 | 1850 | 6.1313 | | 6.1305 | 2.0 | 3700 | 6.1243 | | 6.1109 | 3.0 | 5550 | 6.1201 | | 588589ae8aeea0ca2d5552e7aca0e7e7 |
apache-2.0 | ['translation'] | false | opus-mt-sv-lg * source languages: sv * target languages: lg * OPUS readme: [sv-lg](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-lg/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 17397406b12f5ab6c3cc10d27fe407e8 |
apache-2.0 | ['generated_from_keras_callback'] | false | pmfsl/multi-bert-base-finetuned-rte 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: 0.4024 - Validation Loss: 0.2674 - Train Accuracy: 0.9009 - Train ... | d11d51d85d347a1e8300065b8ee01c6c |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Accuracy | Train F1 | Epoch | |:----------:|:---------------:|:--------------:|:--------:|:-----:| | 0.4024 | 0.2674 | 0.9009 | 0.9013 | 0 | | d655a4706d7c5388ad9fa2e25d28b902 |
mit | ['summarization', 'generated_from_trainer'] | false | mbart-large-50-finetuned-amazon-en-es This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.9825 - Rouge1: 0.1511 - Rouge2: 0.0537 - Rougel: 0.1393 - Rougelsum: 0.1404 | b0ac5e677e138e1a07c21123a21a173a |
mit | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 2.909 | 1.0 | 838 | 2.8106 | 0.1258 | 0.0571 | 0.1248 | 0.1240 | | 1.8102 | 2.0 | 1676 ... | aeb98a5aa9d21be4ea44f339e4c43ddf |
apache-2.0 | ['generated_from_trainer'] | false | skills-classifier 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: 0.3051 - Accuracy: 0.9242 | c492b6a2ceccb4f7fdf43e5626b279be |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 12a5e2fe9b4cff3dba3322a7bece92f5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 312 | 0.2713 | 0.9058 | | 0.361 | 2.0 | 624 | 0.2539 | 0.9182 | | 0.361 | 3.0 | 936 | 0.2802 | 0.... | 4f2730d0e847002e899005b191761ae9 |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_2000k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 0, 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... | c0013bed01def23dceb9b8f1f2076f13 |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-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_0-step_2000k') model = TFBertModel.from_pretrained("google/multib... | 866001ada0adb66dfedbc645838d7329 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | my-korean-stable-diffusion-v1-5 It's [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) model, just text encoder and tokenizer are replaced with my [Bingsu/clip-vit-large-patch14-ko](https://huggingface.co/Bingsu/clip-vit-large-patch14-ko). If you are looking for a Korean diffusi... | f1b30edd3ad7c070a115458d01129b8c |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Usage ```sh pip install transformers accelerate>=0.14.0 diffusers>=0.7.2 ``` ```python import torch from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler repo = "Bingsu/my-korean-stable-diffusion-v1-5" euler_ancestral_scheduler = EulerAncestralDiscreteScheduler.from_config(repo, subfolder="... | b0fc95acec16ade14e1bdf6133fef5da |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | more examples ```python prompt = "고퀄리티 하얀 고양이 사진" seed = 46399 generator = torch.Generator("cuda").manual_seed(seed) pipe(prompt, num_inference_steps=25, generator=generator).images[0] ```  ```python prompt = "고퀄리티 하얀 고양이 사진, 피아노를 치는 중" seed = 12345 generator = torch.Generato... | 65e658a7b1a00c546200e77af14ceb9c |
mit | ['generated_from_keras_callback'] | false | Sushant45/Web_browser-clustered This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1326 - Train End Logits Accuracy: 0.9792 - Train Start Logits Accu... | bfeb2b64201f066a8ae993066f80b69f |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | f493a5578b02d741c94817c43d81af3e |
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: 0.2114 - Accuracy: 0.927 - F1: 0.9268 | 077ce3fc254bee74460c54cbac755415 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8082 | 1.0 | 250 | 0.3065 | 0.9075 | 0.9054 | | 0.2406 | 2.0 | 500 | 0.2114 | 0.927 | 0.9268 | | 9ad80e7004cfbdfb1fe26b701f76fba4 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_unispeech-sat_s115 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your spee... | dbea2922af3d0500eaea368a888a1b52 |
mit | ['conversational'] | false | Model Details **Model Description:** GPT-2 Large is the **774M parameter** version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective. - **Developed by:** OpenAI, see [associated research p... | f7784deae6c58207f87189840277dd30 |
mit | ['conversational'] | false | How to Get Started with the Model Use the code below to get started with the model. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeli... | 68348ef241fd190a095ecca0676694b3 |
mit | ['conversational'] | false | Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclan... | 9fba1b7d1d036eb3d0614feec1be27f9 |
mit | ['conversational'] | false | Results The model achieves the following results without any fine-tuning (zero-shot): | Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW | |:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:| | (me... | e3e4cdfccbe1f089abea4c0bb9a2ff26 |
mit | ['conversational'] | false | Technical Specifications See the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) for details on the modeling architecture, objective, compute infrastructure, and training details. | e1f17f3a64a61e5b708034a1b30907f6 |
mit | [] | false | kawaii_girl_plus_style_v1.1 on Stable Diffusion This is the `<kawaii>` 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.... | f92a81b98410fa7b7e355a1f7e014c51 |
apache-2.0 | ['automatic-speech-recognition', 'ja'] | false | exp_w2v2t_ja_vp-fr_s543 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 8d4e4a8f091423b2130674ea346c99c1 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_wnli This model is a fine-tuned version of [gokuls/mobilebert_sa_pre-training-complete](https://huggingface.co/gokuls/mobilebert_sa_pre-training-complete) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3677 - Accuracy: 0.295... | 11b2d5e8fc6bc9db3b3b7b3407edd23c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3708 | 1.0 | 5 | 0.3927 | 0.3944 | | 0.3555 | 2.0 | 10 | 0.3715 | 0.4225 | | 0.3493 | 3.0 | 15 | 0.3677 | 0.... | 48ab11e91b4ba6872a7d8721b028309a |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_1_binary 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: 1.5992 - F1: 0.7687 | 008c5e9f950cd35b9887981f7bc2c844 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 288 | 0.3960 | 0.7467 | | 0.3988 | 2.0 | 576 | 0.3947 | 0.7487 | | 0.3988 | 3.0 | 864 | 0.4511 | 0.7662 | |... | 028d2123f2f52f3bc911bdb4e55b626d |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53-Total2e-4_3 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2893 - Wer: 0.1863 | 8d1255e09468c932fbf5935c5682cdf9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 82cfc7669c0788faabc5b37e02ac16a3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 5.16 | 0.1 | 200 | 2.9123 | 0.9707 | | 2.4599 | 0.2 | 400 | 0.8145 | 0.6906 | | 1.0523 | 0.3 | 600 | 0.5247 | 0.482... | f4ee4ea58aab21654cbf4face21020c1 |
apache-2.0 | ['image-classification', 'pytorch', 'onnx'] | false | Usage instructions ```python from PIL import Image from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize from torchvision.transforms.functional import InterpolationMode from pyrovision.models import model_from_hf_hub model = model_from_hf_hub("pyronear/resnet18").eval() img =... | 5d89a11bd49ca81b447229caa848b99c |
other | ['generated_from_trainer'] | false | dalio-6.7b-test This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6641 - Accuracy: 0.0662 | a1b8a4c6441c5c4c97f2d38faab7fef7 |
other | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0... | 0b39f79813d5bae70d626bd12048ef0e |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.5958 | 0.31 | 16 | 2.5371 | 0.0659 | | 2.3784 | 0.62 | 32 | 2.5039 | 0.0670 | | 2.3578 | 0.92 | 48 | 2.6074 | 0.... | 551a2d963910813582cd6dfa85e4956a |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/t5-base-subjqa-electronics-qg` This model is fine-tuned version of [lmqg/t5-base-squad](https://huggingface.co/lmqg/t5-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics) via [`lmqg`](https://github.com/asahi41... | 19caa35a35d10ab9cb987cee217627a6 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [lmqg/t5-base-squad](https://huggingface.co/lmqg/t5-base-squad) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (electronics) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/... | a277a5206bcea9db877299af14fed1d8 |
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", "lmqg/t5-base-subjqa-electronics-q... | 0afdbc5965fdc3039c312ddee656fc5e |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-base-subjqa-electronics-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json) | | Score | Type | Dataset ... | 5f14f55b4a39dd8aea85798ac5fad1b8 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: electronics - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: lmqg/t5-base-squad - max_length: 512 - max_length_output: 32 - epoch... | c5ebb91bbdfc7dac3df3477f510e0d7c |
mit | ['generated_from_trainer'] | false | bertimbau-base-lener_br This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the lener_br dataset. It achieves the following results on the evaluation set: - Loss: 0.2298 - Precision: 0.8501 - Recall: 0.9138 - F1: 0.8808 - Accur... | 0f2e60d2e0f8c036c595e99120e05d1b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0686 | 1.0 | 1957 | 0.1399 | 0.7759 | 0.8669 | 0.8189 | 0.9641 | | 0.0437 | 2.0 ... | 249a6c7e32c494f489bac9c4c074b1bd |
cc0-1.0 | ['speechbrain', 'Spoken language understanding'] | false | Fluent Speech Commands The dataset contains real recordings that define a simple spoken language understanding task. You can download it from [here](https://fluent.ai/fluent-speech-commands-a-dataset-for-spoken-language-understanding-research/). The Fluent Speech Commands dataset contains 30,043 utterances from 97 sp... | 7b37e9ecf03c16cd379c5af4a0c336a4 |
cc0-1.0 | ['speechbrain', 'Spoken language understanding'] | false | End-to-end SLU model for Fluent Speech Commands Attention-based RNN sequence-to-sequence model for the [Fluent Speech Commands](https://arxiv.org/pdf/1904.03670.pdf) dataset. This model checkpoint achieves 99.6% accuracy on the test set. The model uses an ASR model trained on LibriSpeech ([`speechbrain/asr-crdnn-rnnl... | c9021febd141bf5ed7e0346b6b3cc484 |
cc0-1.0 | ['speechbrain', 'Spoken language understanding'] | false | >>> '{"action:" "activate"| "object": "lights"| "location": "bedroom"}' ``` The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *decode_file* if needed. Make sure your input tensor is compliant... | c024635ef963fa523054eded3bc1a090 |
cc0-1.0 | ['speechbrain', 'Spoken language understanding'] | false | Training The model was trained with SpeechBrain (f1f421b3). To train it from scratch follows these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ``` cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``` cd recipes/fluen... | 052c3802387a171d87b5e9200c5e6a20 |
cc0-1.0 | ['speechbrain', 'Spoken language understanding'] | false | Referencing Fluent Speech Commands ```bibtex @inproceedings{fluent, author = {Loren Lugosch and Mirco Ravanelli and Patrick Ignoto and Vikrant Singh Tomar and Yoshua Bengio}, editor = {Gernot Kubin and Zdravko Kacic}, title = {... | 50d2f7dedf8bb35490004ee195e51836 |
cc0-1.0 | ['speechbrain', 'Spoken language understanding'] | false | About SpeechBrain SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. Website: https://speechbrain.github.io/ GitHub: https://github.com/speechbrain/speechbrain | 8ddaf197501fee81d9bfb53bf3886cd2 |
openrail | ['text-to-image', 'dreambooth-hackathon', 'wildcard', 'diffusers'] | false | Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run Nail-set-Diffusion: [ Using [EveryDream Finetune Script](https://github.com/victorchall/EveryDream-trainer) for around 10,000 step. | 84f7d2553a28cc3011a3f719da02e372 |
apache-2.0 | ['generated_from_trainer'] | false | mt5-base-coba-coba-coba This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5870 - Rouge1: 0.4336 - Rouge2: 0.288 - Rougel: 0.3746 - Rougelsum: 0.4095 | 778fc883e8a65db9d60a13047247c6cd |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 96e9d54794a1ed7425dc2b6608ff4272 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:| | 7.0922 | 1.0 | 7452 | 0.6538 | 0.3557 | 0.239 | 0.3216 | 0.3342 | | 0.9442 | 2.0 | 14... | 10a491abc79a7edc1fd7c1a700c9ca29 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-Català Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Catalan language using the [Common Voice](https://huggingface.co/datasets/common_voice) and [ParlamentParla](https://www.openslr.org/59/) datasets. **Attention:** The split train/dev/tes... | caadc52820709c403c908521e5d9d6e2 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Results Word error rate was evaluated on the following datasets unseen by the model: | Dataset | WER | | ------- | --- | | [Test split CV+ParlamentParla]((https://github.com/ccoreilly/wav2vec2-catala/blob/master/test.csv)) | 6.92% | | [Google Crowsourced Corpus](https://www.openslr.org/69/) | 12.99% | | Audiobook “L... | a13b36c537b4a7a0c907ffa30595ebff |
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", "ca", split="test[:2%]") processor = Wav2Vec2Processor.from_p... | 89dcd57284e815a4e1f0d9505a19bae1 |
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