license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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agpl-3.0 | [] | false | Info Since people are downloading this and I don't know why, I'll add some information. This model is an image classifier fine-tuned on `microsoft/beit-base-patch16-384`. Its purpose is to be used in the dataset conditioning step for the [Waifu Diffusion project](https://huggingface.co/hakurei/waifu-diffusion), a fine-tune effort for Stable Diffusion. As WD1.4 is planned to have a *significantly large dataset* (~15m images), it is infeasible to analyze every image manually to determine whether or not it should be included in the final training dataset. This image classifier is trained on approximately 3.5k real-life and anime/manga images. Its purpose is to remove aesthetically worthless images from our dataset by classifying them as "`not_aesthetic`". The image classifier was trained to **err on the side of caution** and will generally tend to include images unless they are in a "manga-like" format, have messy lines and/or are sketches, or include an unacceptable amount of text (namely text that covers the primary subject of the image). The idea is that certain images will hurt a SD fine-tune. Note: This classifier is not perfect, just like every other classifier out there. However, with a sufficiently large dataset, any imperfections or misclassifications should average themselves out due to the Law of Large Numbers. You can test out the classifier [here](https://huggingface.co/spaces/cafeai/cafe_aesthetic_demo), along with some other classifiers for the project. | a23c600e77f9da84315c53e097d35d60 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4635 - Wer: 0.3357 | ee609339555a895f78bbe2c7c7c96010 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.6808 | 4.0 | 500 | 1.5478 | 1.0481 | | 0.835 | 8.0 | 1000 | 0.4611 | 0.4703 | | 0.3013 | 12.0 | 1500 | 0.4327 | 0.3887 | | 0.1741 | 16.0 | 2000 | 0.4073 | 0.3677 | | 0.1309 | 20.0 | 2500 | 0.4306 | 0.3595 | | 0.1097 | 24.0 | 3000 | 0.4318 | 0.3475 | | 0.0825 | 28.0 | 3500 | 0.4635 | 0.3357 | | ecda0a8be1333ce57195aa200bc14db2 |
apache-2.0 | ['generated_from_keras_callback'] | false | marian-finetuned-hi-hinglish This model is a fine-tuned version of [Helsinki-NLP/opus-mt-hi-en](https://huggingface.co/Helsinki-NLP/opus-mt-hi-en) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.1869 - Validation Loss: 4.0607 - Epoch: 0 | f256f4a787da2782b3d03998a429eb67 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 279, '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, 'weight_decay_rate': 0.01} - training_precision: float32 | c4f69b0a44a01179b52270b380323826 |
apache-2.0 | ['generated_from_trainer'] | false | distilbart-xsum-12-3-whole_summary_chatGPT_and_tweetsum This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-3](https://huggingface.co/sshleifer/distilbart-xsum-12-3) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7952 - Rouge1: 45.7353 - Rouge2: 29.1566 - Rougel: 45.8429 - Rougelsum: 45.7353 - Gen Len: 16.6 | be5433367d9b6e305739dc8407e85e55 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP | 1334b595ffeb74cad50ed604b015445f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 397 | 2.8069 | 42.233 | 23.7538 | 39.2701 | 39.2701 | 17.0 | | 2.8673 | 2.0 | 794 | 2.7736 | 48.2389 | 29.6927 | 43.5004 | 43.5004 | 17.4 | | 1.8043 | 3.0 | 1191 | 2.7952 | 45.7353 | 29.1566 | 45.8429 | 45.7353 | 16.6 | | 994e462907239a1a35fb5603381018cf |
apache-2.0 | ['tapas', 'table-question-answering'] | false | TAPAS mini model fine-tuned on WikiTable Questions (WTQ) This model has 2 versions which can be used. The default version corresponds to the `tapas_wtq_wikisql_sqa_inter_masklm_mini_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training, and then fine-tuned in a chain on [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253), [WikiSQL](https://github.com/salesforce/WikiSQL) and finally [WTQ](https://github.com/ppasupat/WikiTableQuestions). It uses relative position embeddings (i.e. resetting the position index at every cell of the table). The other (non-default) version which can be used is: - `no_reset`, which corresponds to `tapas_wtq_wikisql_sqa_inter_masklm_mini` (intermediate pre-training, absolute position embeddings). Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by the Hugging Face team and contributors. | 946687b351fc39eac6991033f73fa51b |
apache-2.0 | ['tapas', 'table-question-answering'] | false | Results Size | Reset | Dev Accuracy | Link -------- | --------| -------- | ---- LARGE | noreset | 0.5062 | [tapas-large-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-large-finetuned-wtq/tree/no_reset) LARGE | reset | 0.5097 | [tapas-large-finetuned-wtq](https://huggingface.co/google/tapas-large-finetuned-wtq/tree/main) BASE | noreset | 0.4525 | [tapas-base-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-base-finetuned-wtq/tree/no_reset) BASE | reset | 0.4638 | [tapas-base-finetuned-wtq](https://huggingface.co/google/tapas-base-finetuned-wtq/tree/main) MEDIUM | noreset | 0.4324 | [tapas-medium-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-medium-finetuned-wtq/tree/no_reset) MEDIUM | reset | 0.4324 | [tapas-medium-finetuned-wtq](https://huggingface.co/google/tapas-medium-finetuned-wtq/tree/main) SMALL | noreset | 0.3681 | [tapas-small-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-small-finetuned-wtq/tree/no_reset) SMALL | reset | 0.3762 | [tapas-small-finetuned-wtq](https://huggingface.co/google/tapas-small-finetuned-wtq/tree/main) **MINI** | **noreset** | **0.2783** | [tapas-mini-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-mini-finetuned-wtq/tree/no_reset) **MINI** | **reset** | **0.2854** | [tapas-mini-finetuned-wtq](https://huggingface.co/google/tapas-mini-finetuned-wtq/tree/main) TINY | noreset | 0.0823 | [tapas-tiny-finetuned-wtq (with absolute pos embeddings)](https://huggingface.co/google/tapas-tiny-finetuned-wtq/tree/no_reset) TINY | reset | 0.1039 | [tapas-tiny-finetuned-wtq](https://huggingface.co/google/tapas-tiny-finetuned-wtq/tree/main) | 3352d95357c2ee3d1ca799d86e5895b6 |
apache-2.0 | ['tapas', 'table-question-answering'] | false | Model description TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion. This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of a table and associated text. - Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements. This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed or refuted by the contents of a table. Fine-tuning is done by adding a cell selection head and aggregation head on top of the pre-trained model, and then jointly train these randomly initialized classification heads with the base model on SQa, WikiSQL and finally WTQ. | a6b0938976917e76f240a5bdfb30b005 |
apache-2.0 | ['tapas', 'table-question-answering'] | false | Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Question [SEP] Flattened table [SEP] ``` The authors did first convert the WTQ dataset into the format of SQA using automatic conversion scripts. | 664045fdc860a46eed551de5cfe6ecae |
apache-2.0 | ['tapas', 'table-question-answering'] | false | Fine-tuning The model was fine-tuned on 32 Cloud TPU v3 cores for 50,000 steps with maximum sequence length 512 and batch size of 512. In this setup, fine-tuning takes around 10 hours. The optimizer used is Adam with a learning rate of 1.93581e-5, and a warmup ratio of 0.128960. An inductive bias is added such that the model only selects cells of the same column. This is reflected by the `select_one_column` parameter of `TapasConfig`. See the [paper](https://arxiv.org/abs/2004.02349) for more details (tables 11 and 12). | 75ba476a422e0b46f58a2953dbbc4272 |
apache-2.0 | ['tapas', 'table-question-answering'] | false | BibTeX entry and citation info ```bibtex @misc{herzig2020tapas, title={TAPAS: Weakly Supervised Table Parsing via Pre-training}, author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos}, year={2020}, eprint={2004.02349}, archivePrefix={arXiv}, primaryClass={cs.IR} } ``` ```bibtex @misc{eisenschlos2020understanding, title={Understanding tables with intermediate pre-training}, author={Julian Martin Eisenschlos and Syrine Krichene and Thomas Müller}, year={2020}, eprint={2010.00571}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtex @article{DBLP:journals/corr/PasupatL15, author = {Panupong Pasupat and Percy Liang}, title = {Compositional Semantic Parsing on Semi-Structured Tables}, journal = {CoRR}, volume = {abs/1508.00305}, year = {2015}, url = {http://arxiv.org/abs/1508.00305}, archivePrefix = {arXiv}, eprint = {1508.00305}, timestamp = {Mon, 13 Aug 2018 16:47:37 +0200}, biburl = {https://dblp.org/rec/journals/corr/PasupatL15.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` | 3dbd10272d7889252ddee4bf981b36aa |
apache-2.0 | ['generated_from_trainer'] | false | Negation_Scope_Detection_SFU_Spanish_NLP-CIC-WFU_DisTEMIST_fine_tuned This model is a fine-tuned version of [ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT](https://huggingface.co/ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3219 - Precision: 0.7403 - Recall: 0.7571 - F1: 0.7486 - Accuracy: 0.9518 | a4703b37ce42b684eb049bb7f5f8ff2e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - 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: 7 | beeea04dcd703f461a9575acbaa21569 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 72 | 0.2142 | 0.5227 | 0.6497 | 0.5793 | 0.9267 | | No log | 2.0 | 144 | 0.2019 | 0.625 | 0.7062 | 0.6631 | 0.9420 | | No log | 3.0 | 216 | 0.3089 | 0.6444 | 0.6554 | 0.6499 | 0.9432 | | No log | 4.0 | 288 | 0.2376 | 0.6952 | 0.7345 | 0.7143 | 0.9478 | | No log | 5.0 | 360 | 0.2876 | 0.7037 | 0.7514 | 0.7268 | 0.9538 | | No log | 6.0 | 432 | 0.3077 | 0.7278 | 0.7401 | 0.7339 | 0.9534 | | 0.091 | 7.0 | 504 | 0.3219 | 0.7403 | 0.7571 | 0.7486 | 0.9518 | | f4b332a07f76f18c472263cc5c38cc14 |
mit | [] | false | nouns glasses on Stable Diffusion This is the `<nouns glasses>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:     | 2916bae039ff74a8dd99630cc10ea75f |
mit | ['generated_from_trainer'] | false | QA_model This model is a fine-tuned version of [ukr-models/xlm-roberta-base-uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2761 | 29eb8c0970dc5e2ab3f5b5275e1434f4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5806 | 1.0 | 549 | 1.4431 | | 1.3526 | 2.0 | 1098 | 1.2543 | | 1.0814 | 3.0 | 1647 | 1.2761 | | e1983aa9f0d14a56b5cbce37ac99dc00 |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-cased-finetuned-mrpc This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6274 - Accuracy: 0.6838 - F1: 0.8122 - Combined Score: 0.7480 | 72520d84aefb70e98c76b86be7a2c88e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 | 399fbf615f6a46edeeb045db85a386dc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6441 | 1.0 | 917 | 0.6370 | 0.6838 | 0.8122 | 0.7480 | | 0.6451 | 2.0 | 1834 | 0.6553 | 0.6838 | 0.8122 | 0.7480 | | 0.6428 | 3.0 | 2751 | 0.6332 | 0.6838 | 0.8122 | 0.7480 | | 0.6476 | 4.0 | 3668 | 0.6248 | 0.6838 | 0.8122 | 0.7480 | | 0.6499 | 5.0 | 4585 | 0.6274 | 0.6838 | 0.8122 | 0.7480 | | 5c5eaeffb387bae74d1fd7efbbd0fb19 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 450 - mixed_precision_training: Native AMP | 7ca176b068bdff4eef639ce5ccb4ad30 |
apache-2.0 | ['MRC', 'Natural Questions List', 'xlm-roberta-large'] | false | Model description An XLM-RoBERTa reading comprehension model for List Question Answering using a fine-tuned [xlm-roberta-large](https://huggingface.co/xlm-roberta-large/) model that is further fine-tuned on the list questions in the [Natural Questions](https://huggingface.co/datasets/natural_questions) dataset. | 7a8f9c9728037c3a553fe769ce728f18 |
apache-2.0 | ['MRC', 'Natural Questions List', 'xlm-roberta-large'] | false | Intended uses & limitations You can use the raw model for the reading comprehension task. Biases associated with the pre-existing language model, xlm-roberta-large, that we used may be present in our fine-tuned model, listqa_nq-task-xlm-roberta-large. | 4d011400c06ecfcedc997d136c7efdb5 |
apache-2.0 | ['MRC', 'Natural Questions List', 'xlm-roberta-large'] | false | Usage You can use this model directly with the [PrimeQA](https://github.com/primeqa/primeqa) pipeline for reading comprehension [listqa.ipynb](https://github.com/primeqa/primeqa/blob/main/notebooks/mrc/listqa.ipynb). | a8086c0bb9c9da08beb7812fd8ed47bc |
apache-2.0 | ['MRC', 'Natural Questions List', 'xlm-roberta-large'] | false | BibTeX entry and citation info ```bibtex @article{kwiatkowski-etal-2019-natural, title = "Natural Questions: A Benchmark for Question Answering Research", author = "Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and Toutanova, Kristina and Jones, Llion and Kelcey, Matthew and Chang, Ming-Wei and Dai, Andrew M. and Uszkoreit, Jakob and Le, Quoc and Petrov, Slav", journal = "Transactions of the Association for Computational Linguistics", volume = "7", year = "2019", address = "Cambridge, MA", publisher = "MIT Press", url = "https://aclanthology.org/Q19-1026", doi = "10.1162/tacl_a_00276", pages = "452--466", } ``` ```bibtex @article{DBLP:journals/corr/abs-1911-02116, author = {Alexis Conneau and Kartikay Khandelwal and Naman Goyal and Vishrav Chaudhary and Guillaume Wenzek and Francisco Guzm{\'{a}}n and Edouard Grave and Myle Ott and Luke Zettlemoyer and Veselin Stoyanov}, title = {Unsupervised Cross-lingual Representation Learning at Scale}, journal = {CoRR}, volume = {abs/1911.02116}, year = {2019}, url = {http://arxiv.org/abs/1911.02116}, eprinttype = {arXiv}, eprint = {1911.02116}, timestamp = {Mon, 11 Nov 2019 18:38:09 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1911-02116.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` | 13d9796f4ba64566ad0dbc74ee2085db |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_r-wav2vec2_s79 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](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. | 1ef1dd6275cfdacc401c8182f3127dd2 |
mit | ['vision', 'image-captioning'] | false | GIT (GenerativeImage2Text), base-sized, fine-tuned on TextCaps GIT (short for GenerativeImage2Text) model, base-sized version, fine-tuned on TextCaps. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first released in [this repository](https://github.com/microsoft/GenerativeImage2Text). Disclaimer: The team releasing GIT did not write a model card for this model so this model card has been written by the Hugging Face team. | e7d478470f7d9215c1ddd8b3232b4e8c |
mit | ['vision', 'image-captioning'] | false | Model description GIT is a Transformer decoder conditioned on both CLIP image tokens and text tokens. The model is trained using "teacher forcing" on a lot of (image, text) pairs. The goal for the model is simply to predict the next text token, giving the image tokens and previous text tokens. The model has full access to (i.e. a bidirectional attention mask is used for) the image patch tokens, but only has access to the previous text tokens (i.e. a causal attention mask is used for the text tokens) when predicting the next text token.  This allows the model to be used for tasks like: - image and video captioning - visual question answering (VQA) on images and videos - even image classification (by simply conditioning the model on the image and asking it to generate a class for it in text). | 1254e42ac97453df6c53933de1317e69 |
mit | ['vision', 'image-captioning'] | false | Intended uses & limitations You can use the raw model for image captioning. See the [model hub](https://huggingface.co/models?search=microsoft/git) to look for fine-tuned versions on a task that interests you. | 55bef049d1d6b9b87d8a7b5b246e84f7 |
mit | ['vision', 'image-captioning'] | false | Training data From the paper: > We collect 0.8B image-text pairs for pre-training, which include COCO (Lin et al., 2014), Conceptual Captions (CC3M) (Sharma et al., 2018), SBU (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2016), Conceptual Captions (CC12M) (Changpinyo et al., 2021), ALT200M (Hu et al., 2021a), and an extra 0.6B data following a similar collection procedure in Hu et al. (2021a). => however this is for the model referred to as "GIT" in the paper, which is not open-sourced. This checkpoint is "GIT-base", which is a smaller variant of GIT trained on 10 million image-text pairs. Next, the model was fine-tuned on TextCaps. See table 11 in the [paper](https://arxiv.org/abs/2205.14100) for more details. | f9c9365bc81666255780e494aacdcbe7 |
mit | ['vision', 'image-captioning'] | false | Preprocessing We refer to the original repo regarding details for preprocessing during training. During validation, one resizes the shorter edge of each image, after which center cropping is performed to a fixed-size resolution. Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation. | a0e9bc44101bda70d3f2e5db74f7af94 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased.CEBaB_confounding.uniform.absa.5-class.seed_44 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the OpenTable OPENTABLE-ABSA dataset. It achieves the following results on the evaluation set: - Loss: 0.4180 - Accuracy: 0.8827 - Macro-f1: 0.8804 - Weighted-macro-f1: 0.8826 | 6417394be2ef03b90de6e182f9174056 |
apache-2.0 | [] | false | Model Summary > We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find our resulting models capable of crosslingual generalization to unseen tasks & languages. - **Repository:** [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf) - **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786) - **Point of Contact:** [Niklas Muennighoff](mailto:niklas@hf.co) - **Languages:** Refer to [mc4](https://huggingface.co/datasets/mc4) for pretraining & [xP3](https://huggingface.co/bigscience/xP3) for finetuning language proportions. It understands both pretraining & finetuning languages. - **BLOOMZ & mT0 Model Family:** <div class="max-w-full overflow-auto"> <table> <tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3>xP3</a>. Recommended for prompting in English. </tr> <tr> <td>Parameters</td> <td>300M</td> <td>580M</td> <td>1.2B</td> <td>3.7B</td> <td>13B</td> <td>560M</td> <td>1.1B</td> <td>1.7B</td> <td>3B</td> <td>7.1B</td> <td>176B</td> </tr> <tr> <td>Finetuned Model</td> <td><a href=https://huggingface.co/bigscience/mt0-small>mt0-small</a></td> <td><a href=https://huggingface.co/bigscience/mt0-base>mt0-base</a></td> <td><a href=https://huggingface.co/bigscience/mt0-large>mt0-large</a></td> <td><a href=https://huggingface.co/bigscience/mt0-xl>mt0-xl</a></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-560m>bloomz-560m</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-1b1>bloomz-1b1</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-1b7>bloomz-1b7</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-3b>bloomz-3b</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1>bloomz-7b1</a></td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> </tr> <tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a>. Recommended for prompting in non-English.</th> </tr> <tr> <td>Finetuned Model</td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1-mt>bloomz-7b1-mt</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a></td> </tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/Muennighoff/P3>P3</a>. Released for research purposes only. Strictly inferior to above models!</th> </tr> <tr> <td>Finetuned Model</td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1-p3>bloomz-7b1-p3</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a></td> </tr> <th colspan="12">Original pretrained checkpoints. Not recommended.</th> <tr> <td>Pretrained Model</td> <td><a href=https://huggingface.co/google/mt5-small>mt5-small</a></td> <td><a href=https://huggingface.co/google/mt5-base>mt5-base</a></td> <td><a href=https://huggingface.co/google/mt5-large>mt5-large</a></td> <td><a href=https://huggingface.co/google/mt5-xl>mt5-xl</a></td> <td><a href=https://huggingface.co/google/mt5-xxl>mt5-xxl</a></td> <td><a href=https://huggingface.co/bigscience/bloom-560m>bloom-560m</a></td> <td><a href=https://huggingface.co/bigscience/bloom-1b1>bloom-1b1</a></td> <td><a href=https://huggingface.co/bigscience/bloom-1b7>bloom-1b7</a></td> <td><a href=https://huggingface.co/bigscience/bloom-3b>bloom-3b</a></td> <td><a href=https://huggingface.co/bigscience/bloom-7b1>bloom-7b1</a></td> <td><a href=https://huggingface.co/bigscience/bloom>bloom</a></td> </tr> </table> </div> | 6d13eafa9d6bc7b25e8d2b3001398c7b |
apache-2.0 | [] | false | Intended use We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "*Translate to English: Je t’aime.*", the model will most likely answer "*I love you.*". Some prompt ideas from our paper: - 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评? - Suggest at least five related search terms to "Mạng neural nhân tạo". - Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is "Heroes Come in All Shapes and Sizes". Story (in Spanish): - Explain in a sentence in Telugu what is backpropagation in neural networks. **Feel free to share your generations in the Community tab!** | f843d8395d7101a4a3acfc920940bb34 |
apache-2.0 | [] | false | pip install -q transformers from transformers import AutoModelForSeq2SeqLM, AutoTokenizer checkpoint = "bigscience/mt0-large" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details> | befc2483d4e4f440161890a5380ac57f |
apache-2.0 | [] | false | pip install -q transformers accelerate from transformers import AutoModelForSeq2SeqLM, AutoTokenizer checkpoint = "bigscience/mt0-large" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto") inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details> | 6b37d0ca5a08acc691575dab5757a11e |
apache-2.0 | [] | false | pip install -q transformers accelerate bitsandbytes from transformers import AutoModelForSeq2SeqLM, AutoTokenizer checkpoint = "bigscience/mt0-large" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True) inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details> <!-- Necessary for whitespace --> | 258e5a29a41d93e1a1f2e0d00ddef01e |
apache-2.0 | [] | false | Limitations **Prompt Engineering:** The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "*Translate to English: Je t'aime*" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "*Translate to English: Je t'aime.*", "*Translate to English: Je t'aime. Translation:*" "*What is "Je t'aime." in English?*", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "*Explain in a sentence in Telugu what is backpropagation in neural networks.*". | 204eba589cc0ee010bb49299994b40dd |
apache-2.0 | [] | false | Model - **Architecture:** Same as [mt5-large](https://huggingface.co/google/mt5-large), also refer to the `config.json` file - **Finetuning steps:** 25000 - **Finetuning tokens:** 4.62 billion - **Precision:** bfloat16 | 833338bb239775e46498f688d38aa868 |
apache-2.0 | [] | false | Evaluation We refer to Table 7 from our [paper](https://arxiv.org/abs/2211.01786) & [bigscience/evaluation-results](https://huggingface.co/datasets/bigscience/evaluation-results) for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config. | c588ea9c115fc7eee99893d79ae93246 |
apache-2.0 | [] | false | Citation ```bibtex @misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` | 2b8fd757b91cd4260e9fe6ba3e239024 |
apache-2.0 | [] | false | Install lumi: ``` git clone https://github.com/ontocord/lumi pip install transformers sentencepiece ``` Install models: ``` from lumi.modeling_vlt5 import * from lumi.tokenization_vlt5 import * from lumi.modeling_dalle import * import torch minidalle = DalleModel.from_pretrained("ontocord/minidalle").eval().half().to('cuda') vlt5 = VLT5.from_pretrained("ontocord/vlt5").eval().half().to('cuda') vlt5_tokenizer = VLT5Tokenizer.from_pretrained("ontocord/vlt5") ``` Use: ``` text="""A woman riding a black horse next to a blue fence in central park""" img = minidalle.generate( text=text, image_output=True, token_output=False ) print (vlt5_image2text(vlt5, vlt5_tokenizer, "caption:", img)["text"]) print (vlt5_image2text(vlt5, vlt5_tokenizer, "vqa: what is she riding?", img)["text"]) print (vlt5_image2text(vlt5, vlt5_tokenizer, "vqa: what is the color of the fence?", img)["text"]) print (vlt5_image2text(vlt5, vlt5_tokenizer, "vqa: how many horses are there?", img)["text"]) print (vlt5_image2text(vlt5, vlt5_tokenizer, "vqa: is it a man or woman riding the horse?", img)["text"]) print (vlt5_image2text(vlt5, vlt5_tokenizer, "vqa: are they at the beach?", img)["text"]) print (vlt5_image2text(vlt5, vlt5_tokenizer, "vqa: are they at the city?", img)["text"]) print (vlt5_image2text(vlt5, vlt5_tokenizer, "vqa: are they at the park?", img)["text"]) print (vlt5_image2text(vlt5, vlt5_tokenizer, "vqa: are they in space?", img)["text"]) ``` | 29a868e3f286b9fe148351889dd9934c |
apache-2.0 | ['generated_from_trainer', 'gender'] | false | GFMgenderDetection This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4328 - Accuracy: 0.7971 | f2e0737be668d0c73d8c4e9fc8264676 |
apache-2.0 | ['generated_from_trainer', 'gender'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4591 | 1.0 | 4567 | 0.4502 | 0.7841 | | 0.3915 | 2.0 | 9134 | 0.4328 | 0.7971 | | 4b4c3e9b7d799b07d2dd832c2189b13b |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-NER-favsbot This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the favsbot dataset. It achieves the following results on the evaluation set: - Loss: 1.0572 - Precision: 0.5556 - Recall: 0.4722 - F1: 0.5105 - Accuracy: 0.6900 | eac61ef3ed0d78ba83ae9c91325f80cd |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 | 1caebf317a001f5e186c621ab3e5b611 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 4 | 2.4303 | 0.1448 | 0.3556 | 0.2058 | 0.1855 | | No log | 2.0 | 8 | 2.3220 | 0.1465 | 0.3556 | 0.2075 | 0.1991 | | No log | 3.0 | 12 | 2.1842 | 0.2486 | 0.2389 | 0.2436 | 0.4593 | | No log | 4.0 | 16 | 1.9552 | 0.4 | 0.0111 | 0.0216 | 0.4367 | | No log | 5.0 | 20 | 1.6989 | 0.0 | 0.0 | 0.0 | 0.4321 | | No log | 6.0 | 24 | 1.6532 | 0.5 | 0.0056 | 0.0110 | 0.4344 | | No log | 7.0 | 28 | 1.5724 | 0.3649 | 0.15 | 0.2126 | 0.5045 | | No log | 8.0 | 32 | 1.5164 | 0.3654 | 0.2111 | 0.2676 | 0.5271 | | No log | 9.0 | 36 | 1.4448 | 0.4203 | 0.1611 | 0.2329 | 0.5090 | | No log | 10.0 | 40 | 1.3922 | 0.4833 | 0.1611 | 0.2417 | 0.5158 | | No log | 11.0 | 44 | 1.3409 | 0.5395 | 0.2278 | 0.3203 | 0.5498 | | No log | 12.0 | 48 | 1.2831 | 0.5824 | 0.2944 | 0.3911 | 0.5950 | | No log | 13.0 | 52 | 1.2269 | 0.5714 | 0.3556 | 0.4384 | 0.6335 | | No log | 14.0 | 56 | 1.1766 | 0.5625 | 0.4 | 0.4675 | 0.6606 | | No log | 15.0 | 60 | 1.1408 | 0.5540 | 0.4278 | 0.4828 | 0.6674 | | No log | 16.0 | 64 | 1.1159 | 0.56 | 0.4667 | 0.5091 | 0.6810 | | No log | 17.0 | 68 | 1.0908 | 0.5658 | 0.4778 | 0.5181 | 0.6855 | | No log | 18.0 | 72 | 1.0722 | 0.5658 | 0.4778 | 0.5181 | 0.6923 | | No log | 19.0 | 76 | 1.0615 | 0.5592 | 0.4722 | 0.5120 | 0.6900 | | No log | 20.0 | 80 | 1.0572 | 0.5556 | 0.4722 | 0.5105 | 0.6900 | | 20adfdc7a829c9d22a0654481c553fc9 |
cc-by-sa-4.0 | [] | false | Corpora The following corpora were used for training the model: * Gigafida 2.0 * Kas 1.0 * Janes 1.0 (only Janes-news, Janes-forum, Janes-blog, Janes-wiki subcorpora) * Slovenian parliamentary corpus siParl 2.0 * slWaC | 6f413d93b89808c2c80e976cde695405 |
cc-by-sa-4.0 | [] | false | Changelog 2022-07-21: updated with v2 of the model, the old one is still accesible at [cjvt/legacy-t5-sl-small](https://huggingface.co/cjvt/legacy-t5-sl-small). 2022-09-21: added fast tokenizer (Huggingface's TokenizerFast class, the tokenization remains the same) | e09b0a5bebb801b989a90a5b768002ea |
apache-2.0 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_vp-nl_s632 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 6742d258b2b78a50b8238250d18ec9c4 |
openrail | [] | false | SantaCoder  Play with the model on the [SantaCoder Space Demo](https://huggingface.co/spaces/bigcode/santacoder-demo). | 85568a6736815ff8ad9ad8e4eeb51fdf |
openrail | [] | false | Model Summary This is the Megatron-version of [SantaCoder](https://huggingface.co/bigcode/santacoder). We refer the reader to the [SantaCoder model page](https://huggingface.co/bigcode/santacoder) for full documentation about this model - **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - **Project Website:** [bigcode-project.org](www.bigcode-project.org) - **Paper:** [🎅SantaCoder: Don't reach for the stars!🌟](https://t.co/YV3pzUbYOr) - **Point of Contact:** [contact@bigcode-project.org](mailto:contact@bigcode-project.org) - **Languages:** Python, Java, and JavaScript | 8e947342a6d856798566143ddf808d40 |
openrail | [] | false | Intended use The model was trained on GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. You should phrase commands like they occur in source code such as comments (e.g. ` | 016fc6734d6dac5cdfd3b807acf04ab0 |
openrail | [] | false | Attribution & Other Requirements The pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/santacoder-search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code. | 7f15185f860aae79b13f49efe5bcefe5 |
openrail | [] | false | Limitations The model has been trained on source code in Python, Java, and JavaScript. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. | 44d7f1f17230c4b72432863d831c632d |
openrail | [] | false | Software - **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) - **FP16 if applicable:** [apex](https://github.com/NVIDIA/apex) | 18dac692acf651e3e7bf3c3540a8b2fb |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - 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: 10 - mixed_precision_training: Native AMP | 769c42649972d8e528b5213a0aef75fc |
apache-2.0 | ['bert'] | false | Chinese small pre-trained model MiniRBT In order to further promote the research and development of Chinese information processing, we launched a Chinese small pre-training model MiniRBT based on the self-developed knowledge distillation tool TextBrewer, combined with Whole Word Masking technology and Knowledge Distillation technology. This repository is developed based on：https://github.com/iflytek/MiniRBT You may also interested in, - Chinese LERT: https://github.com/ymcui/LERT - Chinese PERT: https://github.com/ymcui/PERT - Chinese MacBERT: https://github.com/ymcui/MacBERT - Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA - Chinese XLNet: https://github.com/ymcui/Chinese-XLNet - Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer More resources by HFL: https://github.com/iflytek/HFL-Anthology | 3f57f9e99ea81054691ec184e79a6dd3 |
mit | [] | false | gbert-large-germaner This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on the germaner dataset. It achieves the following results on the evaluation set: - precision: 0.8693 - recall: 0.8856 - f1: 0.8774 - accuracy: 0.9784 | 53fcbedb2f831108934cf9e38f82b7e9 |
mit | [] | false | Training hyperparameters The following hyperparameters were used during training: - num_train_epochs: 5 - train_batch_size: 8 - eval_batch_size: 8 - learning_rate: 2e-05 - weight_decay_rate: 0.01 - num_warmup_steps: 0 - fp16: True | afc83cc69f8d409a3d6f645a515f02eb |
apache-2.0 | ['generated_from_trainer'] | false | BART_corrector This model is a fine-tuned version of [ainize/bart-base-cnn](https://huggingface.co/ainize/bart-base-cnn) on a homemade dataset. Each sample of the dataset is an english sentence that has been duplicated 10 times and where random errors (7%) were added. It achieves the following results on the evaluation set: - Loss: 0.0025 - Rouge1: 81.4214 - Rouge2: 80.2027 - Rougel: 81.4202 - Rougelsum: 81.4241 - Gen Len: 19.3962 | e7759bcdb61686208377bf37b5def202 |
apache-2.0 | ['generated_from_trainer'] | false | Intended uses & limitations The goal of this model is to correct a sentence, given several versions of it with various mistakes. Text sample : _TheIdeSbgn of thh Eiffel Toweg is aYtribeted to Ma. . ahd design of The Eijfel Tower is attribQtedBto ta. . The designYof the EifZel Tower Vs APtWibuteQ to Ma. . The xeQign oC the EiffelXTower ik attributed to Ma. . ghebFesign of theSbiffel TJwer is atMributed to Ma. . The desOBn of thQ Eiffel ToweP isfattributnd toBMa. . The design of the EBfUel Fower is JtAriOuted tx Ma. . The design of Jhe ENffel LoweF is aptrVbuted Lo Ma. . The deslgX of the lPffel Towermis attributedhtohMa. . The desRgn of thekSuffel Tower is Ttkribufed to Ma. ._ | f2282b247695be2da1156039a3abcd07 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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: 4 - mixed_precision_training: Native AMP | e8830ffbb2725a9d1c62394cb6a3cebe |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.0071 | 1.0 | 2365 | 0.0039 | 81.3664 | 80.0861 | 81.3601 | 81.3667 | 19.3967 | | 0.0033 | 2.0 | 4730 | 0.0029 | 81.3937 | 80.1548 | 81.3902 | 81.3974 | 19.3961 | | 0.0018 | 3.0 | 7095 | 0.0029 | 81.3838 | 80.1404 | 81.385 | 81.3878 | 19.3965 | | 0.001 | 4.0 | 9460 | 0.0025 | 81.4214 | 80.2027 | 81.4202 | 81.4241 | 19.3962 | | 9aafe3ff59464ad333577fe69f4ec461 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | **megaPals2.1** Hi guys! Do you remember the superhero vintage animated series? Do you like the 70s style? This Stable Diffusion 2.1 embedding is for you! Some recomendations: the magic word for your prompts is megaPals. If you enjoy my work, please consider supporting me: [](https://www.buymeacoffee.com/elrivx) Examples: <img src=https://imgur.com/wZmw8Xr.png width=30% height=30%> <img src=https://imgur.com/JJGBmT8.png width=30% height=30%> <img src=https://imgur.com/0Nr4IJm.png width=30% height=30%> <img src=https://imgur.com/rRN9r1N.png width=30% height=30%> | 3604be4c936a8a9bae1ea5da703bf2f0 |
apache-2.0 | ['generated_from_trainer'] | false | bert-tiny-Massive-intent-KD-BERT_and_distilBERT This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 2.3729 - Accuracy: 0.8470 | 4cfb4f80e5ab2ab657198a9b0d0526b6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP | 218898fceca02847635a4e8619a96d27 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 15.1159 | 1.0 | 720 | 12.8257 | 0.2253 | | 12.9949 | 2.0 | 1440 | 10.9891 | 0.4304 | | 11.3865 | 3.0 | 2160 | 9.5622 | 0.5032 | | 10.0553 | 4.0 | 2880 | 8.3700 | 0.5539 | | 8.9431 | 5.0 | 3600 | 7.4127 | 0.6104 | | 8.0135 | 6.0 | 4320 | 6.6185 | 0.6286 | | 7.1987 | 7.0 | 5040 | 5.9517 | 0.6818 | | 6.5168 | 8.0 | 5760 | 5.3879 | 0.7118 | | 5.9352 | 9.0 | 6480 | 4.9426 | 0.7275 | | 5.4299 | 10.0 | 7200 | 4.5637 | 0.7413 | | 5.0017 | 11.0 | 7920 | 4.2379 | 0.7585 | | 4.5951 | 12.0 | 8640 | 3.9699 | 0.7678 | | 4.2849 | 13.0 | 9360 | 3.7416 | 0.7737 | | 3.991 | 14.0 | 10080 | 3.5502 | 0.7865 | | 3.7455 | 15.0 | 10800 | 3.4090 | 0.7900 | | 3.5315 | 16.0 | 11520 | 3.3053 | 0.7914 | | 3.345 | 17.0 | 12240 | 3.1670 | 0.8003 | | 3.1767 | 18.0 | 12960 | 3.0739 | 0.8013 | | 3.0322 | 19.0 | 13680 | 2.9927 | 0.8047 | | 2.8864 | 20.0 | 14400 | 2.9366 | 0.8037 | | 2.7728 | 21.0 | 15120 | 2.8666 | 0.8091 | | 2.6732 | 22.0 | 15840 | 2.8146 | 0.8126 | | 2.5726 | 23.0 | 16560 | 2.7588 | 0.8195 | | 2.493 | 24.0 | 17280 | 2.7319 | 0.8273 | | 2.4183 | 25.0 | 18000 | 2.6847 | 0.8249 | | 2.3526 | 26.0 | 18720 | 2.6317 | 0.8323 | | 2.2709 | 27.0 | 19440 | 2.6071 | 0.8288 | | 2.2125 | 28.0 | 20160 | 2.5982 | 0.8323 | | 2.1556 | 29.0 | 20880 | 2.5546 | 0.8337 | | 2.1042 | 30.0 | 21600 | 2.5278 | 0.8318 | | 2.054 | 31.0 | 22320 | 2.5005 | 0.8411 | | 2.0154 | 32.0 | 23040 | 2.4891 | 0.8347 | | 1.9785 | 33.0 | 23760 | 2.4633 | 0.8367 | | 1.9521 | 34.0 | 24480 | 2.4451 | 0.8421 | | 1.9247 | 35.0 | 25200 | 2.4370 | 0.8416 | | 1.8741 | 36.0 | 25920 | 2.4197 | 0.8446 | | 1.8659 | 37.0 | 26640 | 2.4081 | 0.8406 | | 1.8367 | 38.0 | 27360 | 2.3979 | 0.8426 | | 1.8153 | 39.0 | 28080 | 2.3758 | 0.8451 | | 1.7641 | 40.0 | 28800 | 2.3729 | 0.8470 | | 1.7608 | 41.0 | 29520 | 2.3683 | 0.8460 | | 1.7647 | 42.0 | 30240 | 2.3628 | 0.8446 | | 1.7656 | 43.0 | 30960 | 2.3492 | 0.8470 | | ba3882c92bca1cdbdb76b7102139424b |
apache-2.0 | ['automatic-speech-recognition', 'fa'] | false | exp_w2v2t_fa_xlsr-53_s204 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (fa)](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. | 0e87144618d10fff7753350d7b711cc4 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.0981 - Accuracy: 0.9801 | 03bdfd3c5555b00a474ce57a0dc0a156 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6641 | 1.0 | 399 | 0.5522 | 0.9337 | | 0.2698 | 2.0 | 798 | 0.2015 | 0.9715 | | 0.1839 | 3.0 | 1197 | 0.1195 | 0.9793 | | 0.1582 | 4.0 | 1596 | 0.1039 | 0.9791 | | 0.1425 | 5.0 | 1995 | 0.0981 | 0.9801 | | bae576a1ab8ec5ed7ac5f6a0722496fc |
mit | ['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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 | 83c6bd32ba6cbba1e4a5b4e8ee99f182 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.7384 | 0.61 | 500 | 1.6251 | | 0.0325 | 1.22 | 1000 | 0.0146 | | 0.0104 | 1.83 | 1500 | 0.0094 | | 0.008 | 2.44 | 2000 | 0.0074 | | 0.0061 | 3.05 | 2500 | 0.0058 | | 0.0057 | 3.66 | 3000 | 0.0050 | | 0.0059 | 4.27 | 3500 | 0.0050 | | 0.0047 | 4.88 | 4000 | 0.0050 | | 0.0043 | 5.49 | 4500 | 0.0045 | | 0.0043 | 6.11 | 5000 | 0.0045 | | 0.0036 | 6.72 | 5500 | 0.0043 | | 0.0038 | 7.33 | 6000 | 0.0041 | | 0.0034 | 7.94 | 6500 | 0.0044 | | 0.0036 | 8.55 | 7000 | 0.0040 | | 0.0032 | 9.16 | 7500 | 0.0039 | | 0.0033 | 9.77 | 8000 | 0.0037 | | 0.0032 | 10.38 | 8500 | 0.0036 | | 0.0029 | 10.99 | 9000 | 0.0035 | | 0.003 | 11.6 | 9500 | 0.0035 | | 0.0027 | 12.21 | 10000 | 0.0036 | | 4971b45cdbb3bada17ad340b0422e709 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-samsum-ElectrifAi_v8.3 This model is a fine-tuned version of [philschmid/bart-large-cnn-samsum](https://huggingface.co/philschmid/bart-large-cnn-samsum) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8755 - Rouge1: 60.4165 - Rouge2: 41.6463 - Rougel: 50.9083 - Rougelsum: 59.2499 - Gen Len: 109.7 | 90d48ed4962998eb6b8ba8c9e8017b3b |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP | d0cc222206520cf10166488a7226186b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 20 | 0.9037 | 57.105 | 36.4038 | 46.3683 | 55.8701 | 99.15 | | No log | 2.0 | 40 | 0.8759 | 58.7016 | 39.3877 | 47.444 | 57.4063 | 113.8 | | No log | 3.0 | 60 | 0.8755 | 60.4165 | 41.6463 | 50.9083 | 59.2499 | 109.7 | | e2f62361969c82abba83145acbcd3eb9 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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: 2 | 12628cab2af629647877f967a9f62273 |
apache-2.0 | ['translation'] | false | opus-mt-sv-hu * source languages: sv * target languages: hu * OPUS readme: [sv-hu](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-hu/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/sv-hu/opus-2020-01-26.zip) * test set translations: [opus-2020-01-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-hu/opus-2020-01-26.test.txt) * test set scores: [opus-2020-01-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-hu/opus-2020-01-26.eval.txt) | 36746184364b9452a503d8013f870d5d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_stsb_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.2659 - Pearson: nan - Spearmanr: nan - Combined Score: nan | 0fc79509782d43c2dc1b4a31d70c78bc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 7.0456 | 1.0 | 23 | 4.3280 | nan | nan | nan | | 4.7979 | 2.0 | 46 | 3.4200 | nan | nan | nan | | 3.7359 | 3.0 | 69 | 2.7494 | nan | nan | nan | | 2.9308 | 4.0 | 92 | 2.3396 | nan | nan | nan | | 2.3776 | 5.0 | 115 | 2.2659 | nan | nan | nan | | 2.1865 | 6.0 | 138 | 2.3171 | nan | nan | nan | | 2.1731 | 7.0 | 161 | 2.3598 | nan | nan | nan | | 2.1793 | 8.0 | 184 | 2.4690 | 0.1389 | 0.1432 | 0.1410 | | 2.1725 | 9.0 | 207 | 2.3589 | 0.0899 | 0.0808 | 0.0854 | | 2.1621 | 10.0 | 230 | 2.3156 | 0.0853 | 0.0802 | 0.0827 | | 72b091739ce90a26c7ae0df17f0c8b3b |
apache-2.0 | ['summarization', 'persian', 'generated_from_trainer'] | false | mt5-base-finetuned-persian This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 3.6086 - Rouge-1: 22.02 - Rouge-2: 7.41 - Rouge-l: 18.95 - Gen Len: 19.0 - Bertscore: 69.89 | f61555e0fcb1bf88941a1e2268e33b02 |
apache-2.0 | ['summarization', 'persian', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - label_smoothing_factor: 0.1 | c8ee7165a625518e7a4018396bb07122 |
apache-2.0 | ['summarization', 'persian', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:| | 7.2823 | 0.96 | 19 | 3.9800 | 19.78 | 5.57 | 16.24 | 19.0 | 68.19 | | 4.7334 | 1.96 | 38 | 3.7620 | 20.92 | 7.49 | 18.27 | 18.91 | 68.72 | | 4.3891 | 2.96 | 57 | 3.6349 | 21.07 | 7.66 | 18.53 | 18.96 | 69.73 | | 4.2 | 3.96 | 76 | 3.6315 | 19.63 | 6.49 | 16.61 | 19.0 | 69.15 | | 3.9202 | 4.96 | 95 | 3.6086 | 21.2 | 6.8 | 17.06 | 19.0 | 69.48 | | 97ff1513e0575c799f5e38ed996bfd43 |
mit | [] | false | Model description LegalBert is a BERT-base-cased model fine-tuned on a subset of the `case.law` corpus. Further details can be found in this paper: [A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering](http://ceur-ws.org/Vol-2645/paper5.pdf) Nils Holzenberger, Andrew Blair-Stanek and Benjamin Van Durme *Proceedings of the 2020 Natural Legal Language Processing (NLLP) Workshop, 24 August 2020* | d56f7078aaaf8722cc29b6d179c770b1 |
mit | [] | false | Citation ``` @inproceedings{holzenberger20dataset, author = {Nils Holzenberger and Andrew Blair{-}Stanek and Benjamin Van Durme}, title = {A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering}, booktitle = {Proceedings of the Natural Legal Language Processing Workshop 2020 co-located with the 26th {ACM} {SIGKDD} International Conference on Knowledge Discovery {\&} Data Mining {(KDD} 2020), Virtual Workshop, August 24, 2020}, series = {{CEUR} Workshop Proceedings}, volume = {2645}, pages = {31--38}, publisher = {CEUR-WS.org}, year = {2020}, url = {http://ceur-ws.org/Vol-2645/paper5.pdf}, } ``` | eae285db53493733b262f55f5dbc7dbe |
other | [] | false | Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neural Architecture Search (NAS) to automatically search architecture hyper-parameters for efficient pre-trained language models (at least 6x faster than BERT-base). AutoTinyBERT provides a model zoo that can meet different latency requirements. | 08d91179d634feb02b85ef1fceec63ca |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_multiple_datasets'] | false | whisper-small-mn-12 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2949 - Wer: 32.3301 - Cer: 13.3493 | 362f5b101e026c0f1590edadacc6c713 |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_multiple_datasets'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 25000 - mixed_precision_training: Native AMP | 31d355b97aacd6aaef9eccf2e373f28c |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_multiple_datasets'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.3012 | 1.05 | 1000 | 0.3749 | 43.2379 | 17.6739 | | 0.2171 | 2.11 | 2000 | 0.3012 | 36.7435 | 15.2029 | | 0.1732 | 3.16 | 3000 | 0.2823 | 33.4225 | 13.7561 | | 0.145 | 4.21 | 4000 | 0.2822 | 32.4995 | 13.2436 | | 0.1159 | 5.27 | 5000 | 0.2949 | 32.3301 | 13.3493 | | 0.0863 | 6.32 | 6000 | 0.3116 | 32.7234 | 13.3892 | | 0.0685 | 7.38 | 7000 | 0.3343 | 32.4776 | 13.3077 | | 0.0506 | 8.43 | 8000 | 0.3584 | 33.3952 | 13.7736 | | 0.0336 | 9.48 | 9000 | 0.3861 | 33.7011 | 13.8493 | | 0.0215 | 10.54 | 10000 | 0.4193 | 33.7011 | 14.0140 | | 0.0141 | 11.59 | 11000 | 0.4463 | 34.0343 | 14.0298 | | 0.0089 | 12.64 | 12000 | 0.4660 | 33.6137 | 13.8052 | | 0.0057 | 13.7 | 13000 | 0.4913 | 33.9797 | 13.9849 | | 0.0039 | 14.75 | 14000 | 0.5078 | 33.9906 | 14.0656 | | 0.0033 | 15.81 | 15000 | 0.5244 | 33.7721 | 13.9192 | | 0.0024 | 16.86 | 16000 | 0.5358 | 33.7612 | 13.7910 | | 0.0018 | 17.91 | 17000 | 0.5469 | 33.6465 | 13.8468 | | 0.0013 | 18.97 | 18000 | 0.5614 | 33.6683 | 13.7553 | | 0.0014 | 20.02 | 19000 | 0.5707 | 33.6574 | 13.8884 | | 0.0006 | 21.07 | 20000 | 0.5835 | 34.0671 | 14.0764 | | 0.0007 | 22.13 | 21000 | 0.5927 | 33.9742 | 14.0772 | | 0.0005 | 23.18 | 22000 | 0.5994 | 34.0398 | 14.0290 | | 0.0004 | 24.24 | 23000 | 0.6067 | 33.9469 | 13.9217 | | 0.0003 | 25.29 | 24000 | 0.6109 | 33.9688 | 13.9591 | | 0.0003 | 26.34 | 25000 | 0.6130 | 33.8267 | 13.8360 | | 7ad0a9b6b165455014b1277e72c8c2b0 |
apache-2.0 | ['translation'] | false | opus-mt-es-zai * source languages: es * target languages: zai * OPUS readme: [es-zai](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-zai/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-zai/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-zai/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/es-zai/opus-2020-01-16.eval.txt) | 9c2e78ea7586620b975bd3e4466b17a1 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | xslr-commonvoice This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.3835 - Wer: 0.3450 | 486a40bae7831766c51704bffe815447 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15.0 - mixed_precision_training: Native AMP | d411ee372ee67f7a436b6020c34edfe7 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.92 | 100 | 3.5761 | 1.0 | | No log | 1.83 | 200 | 3.0512 | 0.9999 | | No log | 2.75 | 300 | 1.0185 | 0.8188 | | No log | 3.67 | 400 | 0.5936 | 0.6411 | | 3.2139 | 4.59 | 500 | 0.4986 | 0.5267 | | 3.2139 | 5.5 | 600 | 0.4327 | 0.4732 | | 3.2139 | 6.42 | 700 | 0.4227 | 0.4462 | | 3.2139 | 7.34 | 800 | 0.4213 | 0.4291 | | 3.2139 | 8.26 | 900 | 0.4016 | 0.4033 | | 0.22 | 9.17 | 1000 | 0.3987 | 0.3825 | | 0.22 | 10.09 | 1100 | 0.4065 | 0.3867 | | 0.22 | 11.01 | 1200 | 0.3929 | 0.3842 | | 0.22 | 11.93 | 1300 | 0.3775 | 0.3687 | | 0.22 | 12.84 | 1400 | 0.3891 | 0.3536 | | 0.1005 | 13.76 | 1500 | 0.3850 | 0.3492 | | 0.1005 | 14.68 | 1600 | 0.3823 | 0.3441 | | e260f9ebae9f85f9700fbc4d9052b87f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 10 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 20 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 2 | 3a022afc7cc8d0b716bde962910afec2 |
mit | [] | false | dovin-baan on Stable Diffusion This is the `<dovin-baan>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:                  | 68b217a64c57302ed1af1736990e44ef |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_distilgpt2_sst2_negation0.001_pretrainedTrue_epochs3 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 3.2638 | 044456757fdd061b2f1d1f390d7e8698 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6836 | 1.0 | 1322 | 3.2638 | | 2.5043 | 2.0 | 2644 | 3.2590 | | 2.4514 | 3.0 | 3966 | 3.2638 | | 4c3f959bd7c1be51dd2ecc910065637d |
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