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://object.pouta.csc.fi/OPUS-MT-models/es-st/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-st/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-st/opus-2020-01-16.eval.txt) | 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_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 | 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.co/Linaqruf/anything-v3.0),and merge back to anything after training. This model is converted from [poison](https://huggingface.co/Fansy/poison) Compare result:  | 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") prompt = "High quality photo of an astronaut riding a horse in space" image = pipe(prompt, num_inference_steps=25).images[0] image.save("astronaut.png") ``` | 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 | 18.997 | | 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 Language Inference (NLI) datasets, with totaling 1,014,787 samples. | 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 | 88.67 | | 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' textb='今天心情不好' output=model(torch.tensor([tokenizer.encode(texta,textb)])) print(torch.nn.functional.softmax(output.logits,dim=-1)) ``` | 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 | 0.5786 | | 0.872 | 4.0 | 12272 | 0.8843 | 0.5967 | | 0.8531 | 5.0 | 15340 | 0.8807 | 0.5959 | | 0.8359 | 6.0 | 18408 | 0.8763 | 0.5999 | | 0.8197 | 7.0 | 21476 | 0.8815 | 0.6009 | | 0.8028 | 8.0 | 24544 | 0.9012 | 0.5934 | | 0.786 | 9.0 | 27612 | 0.8633 | 0.6191 | | 0.769 | 10.0 | 30680 | 0.8734 | 0.6098 | | 0.752 | 11.0 | 33748 | 0.8682 | 0.6220 | | 0.736 | 12.0 | 36816 | 0.8741 | 0.6175 | | 0.7204 | 13.0 | 39884 | 0.8994 | 0.6048 | | 0.7038 | 14.0 | 42952 | 0.8940 | 0.6079 | | 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 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 a `style`:      | 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://object.pouta.csc.fi/OPUS-MT-models/it-es/opus-2020-01-26.zip) * test set translations: [opus-2020-01-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/it-es/opus-2020-01-26.test.txt) * test set scores: [opus-2020-01-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/it-es/opus-2020-01-26.eval.txt) | 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://huggingface.co/OlafII/cosmosx/resolve/main/images/01198-693125065-cosmosx, lion.png" width="100%"/> | 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_precision_training: Native AMP | 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 | | 1.8086 | 200.0 | 800 | 1.0896 | 0.7123 | | 0.2145 | 250.0 | 1000 | 1.1973 | 0.6321 | | 0.0641 | 300.0 | 1200 | 1.3631 | 0.6100 | | 0.0391 | 350.0 | 1400 | 1.4521 | 0.5837 | | 0.0258 | 400.0 | 1600 | 1.3671 | 0.5781 | | 0.0185 | 450.0 | 1800 | 1.3828 | 0.5698 | | 0.0107 | 500.0 | 2000 | 1.4402 | 0.5463 | | 0.0099 | 550.0 | 2200 | 1.5724 | 0.5477 | | 0.0058 | 600.0 | 2400 | 1.5387 | 0.5380 | | 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 its preprocessing were originally published by - Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova: ["BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"](https://arxiv.org/abs/1810.04805), 2018. This model uses a vocabulary for English extracted from the Wikipedia and BooksCorpus (same as in the models by the original BERT authors). Text inputs have been normalized the "cased" way, meaning that the distinction between lower and upper case as well as accent markers have been preserved. This model has no trainable parameters and can be used in an input pipeline outside the training loop. | 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 `[batch_size, ...]` with rank 2 or 3. | 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 with padding tokens. If an input sequence would exceed `seq_length`, the tokenized segments in it are truncated to prefixes of approximately equal sizes to fit exactly. The `encoder_inputs` are a dict of three int32 Tensors, all with shape `[batch_size, seq_length]`, whose elements represent the batch of input sequences as follows: - `"input_word_ids"`: has the token ids of the input sequences. - `"input_mask"`: has value 1 at the position of all input tokens present before padding and value 0 for the padding tokens. - `"input_type_ids"`: has the index of the input segment that gave rise to the input token at the respective position. The first input segment (index 0) includes the start-of-sequence token and its end-of-segment token. The second segment (index 1, if present) includes its end-of-segment token. Padding tokens get index 0 again. | 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_segment_id"` (aka. [SEP]) and `"mask_id"`. This allows users to replace `preprocessor.bert_pack_inputs()` with Python code such as `text.combine_segments()`, possibly `text.masked_language_model()`, and `text.pad_model_inputs()` from the [TensorFlow Text](https://github.com/tensorflow/text) library. | 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 originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Translation (transformer-big) - **Release**: 2022-07-26 - **License:** CC-BY-4.0 - **Language(s):** - Source Language(s): deu - Target Language(s): spa - Language Pair(s): deu-spa - Valid Target Language Labels: - **Original Model**: [opusTCv20210807_transformer-big_2022-07-26.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-spa/opusTCv20210807_transformer-big_2022-07-26.zip) - **Resources for more information:** - [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) - More information about released models for this language pair: [OPUS-MT deu-spa README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/deu-spa/README.md) - [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian) - [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/ | 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(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) | 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-spa/opusTCv20210807_transformer-big_2022-07-26.zip) - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) | 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/deu-spa/opusTCv20210807_transformer-big_2022-07-26.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | | 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 | | deu-spa | newstest2009 | 0.53934 | 25.9 | 2525 | 68111 | | deu-spa | newstest2010 | 0.60102 | 33.8 | 2489 | 65480 | | deu-spa | newstest2011 | 0.57133 | 31.3 | 3003 | 79476 | | deu-spa | newstest2012 | 0.58119 | 32.6 | 3003 | 79006 | | deu-spa | newstest2013 | 0.57559 | 32.4 | 3000 | 70528 | | 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/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-3](https://hf.co/multberts-seed-3). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). | 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 like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 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://object.pouta.csc.fi/OPUS-MT-models/sv-lg/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-lg/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-lg/opus-2020-01-16.eval.txt) | 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 F1: 0.9013 - Epoch: 0 | 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 | 2.8872 | 0.1382 | 0.0675 | 0.1345 | 0.1353 | | 1.0773 | 3.0 | 2514 | 3.3501 | 0.1528 | 0.0658 | 0.1504 | 0.1504 | | 0.5431 | 4.0 | 3352 | 3.9495 | 0.1201 | 0.0561 | 0.1153 | 0.1147 | | 0.2371 | 5.0 | 4190 | 4.5519 | 0.1559 | 0.0732 | 0.1473 | 0.1464 | | 0.0934 | 6.0 | 5028 | 4.7016 | 0.1531 | 0.0634 | 0.1467 | 0.1453 | | 0.0375 | 7.0 | 5866 | 4.9661 | 0.1532 | 0.0562 | 0.1426 | 0.1421 | | 0.0155 | 8.0 | 6704 | 4.9825 | 0.1511 | 0.0537 | 0.1393 | 0.1404 | | 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 | 0.9238 | | 0.1532 | 4.0 | 1248 | 0.3058 | 0.9202 | | 0.0899 | 5.0 | 1560 | 0.3051 | 0.9242 | | 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 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 | 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/multiberts-seed_0-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_0-step_2000k') model = BertModel.from_pretrained("google/multiberts-seed_0-step_2000k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 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 diffusion model that works well in practice, see: - [BAAI/AltDiffusion-m9](https://huggingface.co/BAAI/AltDiffusion-m9) - [Multilingual Stable Diffusion Pipeline](https://github.com/huggingface/diffusers/tree/main/examples/community | 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="scheduler") pipe = StableDiffusionPipeline.from_pretrained( repo, scheduler=euler_ancestral_scheduler, torch_dtype=torch.float16, ) pipe.to("cuda") ``` ```python prompt = "화성에서 말을 타고 있는 우주인 사진" seed = 23957 generator = torch.Generator("cuda").manual_seed(seed) image = pipe(prompt, num_inference_steps=25, generator=generator).images[0] ``` ```python image ```  | 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.Generator("cuda").manual_seed(seed) pipe(prompt, num_inference_steps=25, generator=generator).images[0] ```  ```python prompt = "달과 별이 보이는 밤하늘을 배경으로 한 해변가 사진" seed = 1234246 generator = torch.Generator("cuda").manual_seed(seed) pipe(prompt, num_inference_steps=25, generator=generator).images[0] ```  | 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 Accuracy: 0.9444 - Validation Loss: 0.3331 - Validation End Logits Accuracy: 0.6667 - Validation Start Logits Accuracy: 1.0 - Epoch: 0 | 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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.1326 | 0.9792 | 0.9444 | 0.3331 | 0.6667 | 1.0 | 0 | | 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 speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 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 paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and [GitHub repo](https://github.com/openai/gpt-2) for model developers. - **Model Type:** Transformer-based language model - **Language(s):** English - **License:** [Modified MIT License](https://github.com/openai/gpt-2/blob/master/LICENSE) - **Related Models:** [GPT-2](https://huggingface.co/gpt2), [GPT-Medium](https://huggingface.co/gpt2-medium) and [GPT-XL](https://huggingface.co/gpt2-xl) - **Resources for more information:** - [Research Paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) - [OpenAI Blog Post](https://openai.com/blog/better-language-models/) - [GitHub Repo](https://github.com/openai/gpt-2) - [OpenAI Model Card for GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md) - Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large | 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 = pipeline('text-generation', model='gpt2-large') >>> set_seed(42) >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) [{'generated_text': "Hello, I'm a language model, I can do language modeling. In fact, this is one of the reasons I use languages. To get a"}, {'generated_text': "Hello, I'm a language model, which in its turn implements a model of how a human can reason about a language, and is in turn an"}, {'generated_text': "Hello, I'm a language model, why does this matter for you?\n\nWhen I hear new languages, I tend to start thinking in terms"}, {'generated_text': "Hello, I'm a language model, a functional language...\n\nI don't need to know anything else. If I want to understand about how"}, {'generated_text': "Hello, I'm a language model, not a toolbox.\n\nIn a nutshell, a language model is a set of attributes that define how"}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large') model = GPT2Model.from_pretrained('gpt2-large') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import GPT2Tokenizer, TFGPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large') model = TFGPT2Model.from_pretrained('gpt2-large') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` | 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://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2-large') >>> set_seed(42) >>> generator("The man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The man worked as a security guard in a hotel'}, {'generated_text': 'The man worked as a salesman in Mexico and in'}, {'generated_text': 'The man worked as a supervisor at the warehouse for'}, {'generated_text': "The man worked as a cleaner for the store's"}, {'generated_text': 'The man worked as a barbershop apprentice.'}] >>> set_seed(42) >>> generator("The woman worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The woman worked as a clerk at the bank.'}, {'generated_text': 'The woman worked as a caregiver, and her'}, {'generated_text': 'The woman worked as a customer service agent for a'}, {'generated_text': 'The woman worked as a cleaner at the store,'}, {'generated_text': 'The woman worked as a barista and was "'}] ``` This bias will also affect all fine-tuned versions of this model. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. | 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 | |:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:| | (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) | | | 10.87 | 60.12 | 93.45 | 88.0 | 19.93 | 40.31 | 0.97 | 1.02 | 22.05 | 44.575| | 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. 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 a `style`:                                       | 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 your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 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: 0.2958 | 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 | 0.2958 | | 0.3485 | 4.0 | 20 | 0.3704 | 0.3803 | | 0.3454 | 5.0 | 25 | 0.3815 | 0.2394 | | 0.3461 | 6.0 | 30 | 0.3878 | 0.2394 | | 0.3432 | 7.0 | 35 | 0.3962 | 0.2535 | | 0.3427 | 8.0 | 40 | 0.4050 | 0.1972 | | 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 | | 0.1853 | 4.0 | 1152 | 0.7226 | 0.7285 | | 0.1853 | 5.0 | 1440 | 0.9398 | 0.7334 | | 0.0827 | 6.0 | 1728 | 1.0547 | 0.7427 | | 0.0287 | 7.0 | 2016 | 1.1602 | 0.7563 | | 0.0287 | 8.0 | 2304 | 1.3332 | 0.7171 | | 0.0219 | 9.0 | 2592 | 1.3429 | 0.7420 | | 0.0219 | 10.0 | 2880 | 1.2603 | 0.7648 | | 0.0139 | 11.0 | 3168 | 1.4126 | 0.7569 | | 0.0139 | 12.0 | 3456 | 1.3195 | 0.7483 | | 0.0115 | 13.0 | 3744 | 1.4356 | 0.7491 | | 0.0035 | 14.0 | 4032 | 1.5693 | 0.7636 | | 0.0035 | 15.0 | 4320 | 1.4071 | 0.7662 | | 0.0071 | 16.0 | 4608 | 1.4561 | 0.7579 | | 0.0071 | 17.0 | 4896 | 1.5405 | 0.7634 | | 0.0041 | 18.0 | 5184 | 1.5862 | 0.7589 | | 0.0041 | 19.0 | 5472 | 1.6782 | 0.76 | | 0.0024 | 20.0 | 5760 | 1.5699 | 0.7677 | | 0.0006 | 21.0 | 6048 | 1.5991 | 0.7467 | | 0.0006 | 22.0 | 6336 | 1.6205 | 0.7682 | | 0.0003 | 23.0 | 6624 | 1.6334 | 0.7643 | | 0.0003 | 24.0 | 6912 | 1.5992 | 0.7687 | | 0.0011 | 25.0 | 7200 | 1.6053 | 0.7624 | | 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_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP | 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 | 0.4823 | | 0.8965 | 0.4 | 800 | 0.4391 | 0.4416 | | 0.7994 | 0.5 | 1000 | 0.3889 | 0.3773 | | 0.7491 | 0.6 | 1200 | 0.3604 | 0.3305 | | 0.7425 | 0.7 | 1400 | 0.3543 | 0.3277 | | 0.7253 | 0.8 | 1600 | 0.3397 | 0.3143 | | 0.7221 | 0.9 | 1800 | 0.3341 | 0.2979 | | 0.6853 | 1.0 | 2000 | 0.3244 | 0.2906 | | 0.6107 | 1.1 | 2200 | 0.3127 | 0.2771 | | 0.6233 | 1.2 | 2400 | 0.3116 | 0.2721 | | 0.6214 | 1.3 | 2600 | 0.3256 | 0.2671 | | 0.6511 | 1.4 | 2800 | 0.3019 | 0.2570 | | 0.6491 | 1.5 | 3000 | 0.2961 | 0.2576 | | 0.6411 | 1.6 | 3200 | 0.2963 | 0.2535 | | 0.5963 | 1.7 | 3400 | 0.2939 | 0.2526 | | 0.6146 | 1.8 | 3600 | 0.2908 | 0.2490 | | 0.6291 | 1.9 | 3800 | 0.2851 | 0.2448 | | 0.6154 | 2.0 | 4000 | 0.2861 | 0.2424 | | 0.5652 | 2.1 | 4200 | 0.2852 | 0.2411 | | 0.5648 | 2.2 | 4400 | 0.2856 | 0.2350 | | 0.5365 | 2.3 | 4600 | 0.2802 | 0.2395 | | 0.5855 | 2.4 | 4800 | 0.2883 | 0.2374 | | 0.5978 | 2.5 | 5000 | 0.2855 | 0.2364 | | 0.5863 | 2.6 | 5200 | 0.2736 | 0.2277 | | 0.5569 | 2.7 | 5400 | 0.2746 | 0.2293 | | 0.5628 | 2.8 | 5600 | 0.2719 | 0.2249 | | 0.5655 | 2.9 | 5800 | 0.2653 | 0.2224 | | 0.5578 | 3.0 | 6000 | 0.2685 | 0.2243 | | 0.5303 | 3.1 | 6200 | 0.2696 | 0.2204 | | 0.5316 | 3.2 | 6400 | 0.2733 | 0.2247 | | 0.5476 | 3.3 | 6600 | 0.2716 | 0.2203 | | 0.5326 | 3.4 | 6800 | 0.2697 | 0.2209 | | 0.5375 | 3.5 | 7000 | 0.2701 | 0.2197 | | 0.5364 | 3.6 | 7200 | 0.2655 | 0.2165 | | 0.503 | 3.7 | 7400 | 0.2650 | 0.2125 | | 0.5284 | 3.8 | 7600 | 0.2672 | 0.2162 | | 0.5251 | 3.9 | 7800 | 0.2669 | 0.2172 | | 0.5299 | 4.0 | 8000 | 0.2632 | 0.2081 | | 0.4904 | 4.1 | 8200 | 0.2674 | 0.2099 | | 0.496 | 4.2 | 8400 | 0.2700 | 0.2143 | | 0.5067 | 4.3 | 8600 | 0.2648 | 0.2090 | | 0.506 | 4.4 | 8800 | 0.2595 | 0.2069 | | 0.4795 | 4.5 | 9000 | 0.2653 | 0.2072 | | 0.5149 | 4.6 | 9200 | 0.2618 | 0.2073 | | 0.4786 | 4.7 | 9400 | 0.2632 | 0.2058 | | 0.5056 | 4.8 | 9600 | 0.2674 | 0.2123 | | 0.5059 | 4.9 | 9800 | 0.2642 | 0.2115 | | 0.5119 | 5.0 | 10000 | 0.2672 | 0.2089 | | 0.4619 | 5.1 | 10200 | 0.2658 | 0.2062 | | 0.4647 | 5.2 | 10400 | 0.2664 | 0.2025 | | 0.4707 | 5.3 | 10600 | 0.2656 | 0.2084 | | 0.486 | 5.4 | 10800 | 0.2728 | 0.2029 | | 0.4785 | 5.5 | 11000 | 0.2653 | 0.2004 | | 0.4895 | 5.6 | 11200 | 0.2835 | 0.2119 | | 0.4519 | 5.7 | 11400 | 0.2715 | 0.2061 | | 0.484 | 5.8 | 11600 | 0.2663 | 0.2071 | | 0.4734 | 5.9 | 11800 | 0.2615 | 0.2023 | | 0.4563 | 6.0 | 12000 | 0.2604 | 0.1997 | | 0.4193 | 6.1 | 12200 | 0.2708 | 0.2015 | | 0.4516 | 6.2 | 12400 | 0.2724 | 0.2018 | | 0.4609 | 6.3 | 12600 | 0.2745 | 0.2004 | | 0.43 | 6.4 | 12800 | 0.2716 | 0.1979 | | 0.4424 | 6.5 | 13000 | 0.2674 | 0.1963 | | 0.4589 | 6.6 | 13200 | 0.2622 | 0.1977 | | 0.4458 | 6.7 | 13400 | 0.2668 | 0.1994 | | 0.4233 | 6.8 | 13600 | 0.2739 | 0.1978 | | 0.4557 | 6.9 | 13800 | 0.2692 | 0.1972 | | 0.4472 | 7.0 | 14000 | 0.2686 | 0.1942 | | 0.4193 | 7.1 | 14200 | 0.2843 | 0.1959 | | 0.4033 | 7.2 | 14400 | 0.2767 | 0.1945 | | 0.4266 | 7.3 | 14600 | 0.2808 | 0.1931 | | 0.419 | 7.4 | 14800 | 0.2801 | 0.1945 | | 0.4352 | 7.5 | 15000 | 0.2764 | 0.1934 | | 0.4248 | 7.6 | 15200 | 0.2818 | 0.1938 | | 0.4001 | 7.7 | 15400 | 0.2754 | 0.1931 | | 0.415 | 7.8 | 15600 | 0.2799 | 0.1916 | | 0.4056 | 7.9 | 15800 | 0.2746 | 0.1916 | | 0.419 | 8.0 | 16000 | 0.2789 | 0.1909 | | 0.3974 | 8.1 | 16200 | 0.2913 | 0.1897 | | 0.3999 | 8.2 | 16400 | 0.2894 | 0.1899 | | 0.4179 | 8.3 | 16600 | 0.2819 | 0.1918 | | 0.4081 | 8.4 | 16800 | 0.2868 | 0.1910 | | 0.3963 | 8.5 | 17000 | 0.2835 | 0.1889 | | 0.3748 | 8.6 | 17200 | 0.2841 | 0.1903 | | 0.375 | 8.7 | 17400 | 0.2820 | 0.1874 | | 0.3857 | 8.8 | 17600 | 0.2865 | 0.1872 | | 0.3901 | 8.9 | 17800 | 0.2824 | 0.1882 | | 0.4067 | 9.0 | 18000 | 0.2838 | 0.1887 | | 0.3711 | 9.1 | 18200 | 0.2892 | 0.1897 | | 0.3661 | 9.2 | 18400 | 0.2889 | 0.1883 | | 0.3796 | 9.3 | 18600 | 0.2876 | 0.1886 | | 0.3932 | 9.4 | 18800 | 0.2948 | 0.1877 | | 0.3894 | 9.5 | 19000 | 0.2896 | 0.1884 | | 0.3643 | 9.6 | 19200 | 0.2897 | 0.1868 | | 0.384 | 9.7 | 19400 | 0.2887 | 0.1867 | | 0.3951 | 9.8 | 19600 | 0.2905 | 0.1862 | | 0.3595 | 9.9 | 19800 | 0.2893 | 0.1866 | | 0.3758 | 10.0 | 20000 | 0.2893 | 0.1863 | | 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 = Image.open(path_to_an_image).convert("RGB") | 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-08 - lr_scheduler_type: cosine - num_epochs: 2.0 | 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 | 0.0654 | | 1.3819 | 1.23 | 64 | 2.6680 | 0.0658 | | 1.1529 | 1.54 | 80 | 2.6738 | 0.0665 | | 1.2938 | 1.85 | 96 | 2.6641 | 0.0662 | | 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/asahi417/lm-question-generation). | 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/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) | 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-qg") output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` | 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 | |:-----------|--------:|:------------|:-----------------------------------------------------------------| | BERTScore | 94.26 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_1 | 28.95 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_2 | 21.03 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_3 | 10.73 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | Bleu_4 | 4.55 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | METEOR | 27.39 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | MoverScore | 68.33 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | ROUGE_L | 29.99 | electronics | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | | 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: 4 - batch: 16 - lr: 5e-05 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.0 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-base-subjqa-electronics-qg/raw/main/trainer_config.json). | 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 - Accuracy: 0.9693 | 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 | 3914 | 0.1457 | 0.7997 | 0.8938 | 0.8441 | 0.9623 | | 0.0313 | 3.0 | 5871 | 0.1675 | 0.8466 | 0.8744 | 0.8603 | 0.9651 | | 0.0201 | 4.0 | 7828 | 0.1621 | 0.8713 | 0.8839 | 0.8775 | 0.9718 | | 0.0137 | 5.0 | 9785 | 0.1811 | 0.7783 | 0.9159 | 0.8415 | 0.9645 | | 0.0105 | 6.0 | 11742 | 0.1836 | 0.8568 | 0.9009 | 0.8783 | 0.9692 | | 0.0105 | 7.0 | 13699 | 0.1649 | 0.8339 | 0.9125 | 0.8714 | 0.9725 | | 0.0059 | 8.0 | 15656 | 0.2298 | 0.8501 | 0.9138 | 0.8808 | 0.9693 | | 0.0051 | 9.0 | 17613 | 0.2210 | 0.8437 | 0.9045 | 0.8731 | 0.9693 | | 0.0061 | 10.0 | 19570 | 0.2499 | 0.8627 | 0.8946 | 0.8784 | 0.9681 | | 0.0041 | 11.0 | 21527 | 0.1985 | 0.8560 | 0.9052 | 0.8799 | 0.9720 | | 0.003 | 12.0 | 23484 | 0.2204 | 0.8498 | 0.9065 | 0.8772 | 0.9699 | | 0.0014 | 13.0 | 25441 | 0.2152 | 0.8425 | 0.9067 | 0.8734 | 0.9709 | | 0.0005 | 14.0 | 27398 | 0.2317 | 0.8553 | 0.8987 | 0.8765 | 0.9705 | | 0.0015 | 15.0 | 29355 | 0.2436 | 0.8543 | 0.8989 | 0.8760 | 0.9700 | | 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 speakers. It is recorded as 16 kHz single-channel .wav files each containing a single utterance used for controlling smart-home appliances or virtual assistant, for example, “put on the music” or “turn up the heat in the kitchen”. Each audio is labeled with three slots: action, object, and location. A slot takes on one of the multiple values: for instance, the “location” slot can take on the values “none”, “kitchen”, “bedroom”, or “washroom”. We refer to the combination of slot values as the intent of the utterance. For each intent, there are multiple possible wordings: for example, the intent {action: “activate”, object: “lights”, location: “none”} can be expressed as “turn on the lights”, “switch the lights on”, “lights on”, etc. The dataset has a total of 248 phrasing mapping to 31 unique intents. | 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-rnnlm-librispeech`](https://huggingface.co/speechbrain/asr-crdnn-rnnlm-librispeech)) to extract features from the input audio, then maps these features to an intent and slot labels using a beam search. You can try the model on the `example_fsc.wav` file included here as follows: ```python from speechbrain.pretrained import EndToEndSLU slu = EndToEndSLU.from_hparams("speechbrain/slu-direct-fluent-speech-commands-librispeech-asr") | 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 with the expected sampling rate if you use *encode_batch* and *decode_batch*. | 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/fluent-speech-commands python train.py hparams/train.yaml --data_folder=your_data_folder ``` You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1Zly54252Z218IHJQ9M0B3kTQPZIw_2yC?usp=sharing). | 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 = {Speech Model Pre-Training for End-to-End Spoken Language Understanding}, booktitle = {Proc. of Interspeech}, pages = {814--818}, year = {2019}, } ``` | 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: [](https://huggingface.co/spaces/ringhyacinth/Nail-Diffuser) __Stable Diffusion fine tuned on Nail Set by [Weekend](https://weibo.com/u/5982308498) and [Hyacinth](https://twitter.com/ring_hyacinth).__ Put in a text prompt and generate your own nail set!  > Nail Set, Sunflower (/Irises/Starry Night/Self Portrait) by Van Gogh, Van Gogh color scheme  > Nail Set, hamilton nail, broadway musical theme nail.  > Nail Set, chinese new year nail, super detailed  > Nail Set, thanksgiving nail, super detailed  > Nail set, Disney castle nail, cute Japanese girly nail | 990cf1f06827aed78b7b1a5a40e7070f |
openrail | ['text-to-image', 'dreambooth-hackathon', 'wildcard', 'diffusers'] | false | Model description Trained on [CLIP Ineterrogator captioned dataset](https://huggingface.co/spaces/pharma/CLIP-Interrogator) 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 | 14904 | 0.6900 | 0.427 | 0.2868 | 0.371 | 0.4028 | | 3.0789 | 3.0 | 22356 | 0.6775 | 0.3801 | 0.2581 | 0.34 | 0.3564 | | 1.0565 | 4.0 | 29808 | 0.5928 | 0.4345 | 0.2885 | 0.376 | 0.4102 | | 0.7872 | 5.0 | 37260 | 0.5870 | 0.4336 | 0.288 | 0.3746 | 0.4095 | | 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/test used does not fully map with the CommonVoice 6.1 dataset. A custom split was used combining both the CommonVoice and ParlamentParla dataset and can be found [here](https://github.com/ccoreilly/wav2vec2-catala). Evaluating on the CV test dataset will produce a biased WER as 1144 audio files of that dataset were used in training/evaluation of this model. WER was calculated using this [test.csv](https://github.com/ccoreilly/wav2vec2-catala/blob/master/test.csv) which was not seen by the model during training/evaluation. You can find training and evaluation scripts in the github repository [ccoreilly/wav2vec2-catala](https://github.com/ccoreilly/wav2vec2-catala) When using this model, make sure that your speech input is sampled at 16kHz. | 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 “La llegenda de Sant Jordi” | 13.23% | | 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_pretrained("ccoreilly/wav2vec2-large-xlsr-catala") model = Wav2Vec2ForCTC.from_pretrained("ccoreilly/wav2vec2-large-xlsr-catala") resampler = torchaudio.transforms.Resample(48_000, 16_000) | 89dcd57284e815a4e1f0d9505a19bae1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.