license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 3fa23af00cf5b018091be3d285c2fe83 |
cc-by-sa-4.0 | ['finance'] | false | ELECTRA small Japanese finance generator This is a [ELECTRA](https://github.com/google-research/electra) model pretrained on texts in the Japanese language. The codes for the pretraining are available at [retarfi/language-pretraining](https://github.com/retarfi/language-pretraining/tree/v1.0). | f4a662e92ffeb98d514c4651759ac027 |
cc-by-sa-4.0 | ['finance'] | false | Model architecture The model architecture is the same as ELECTRA small in the [original ELECTRA paper](https://arxiv.org/abs/2003.10555); 12 layers, 64 dimensions of hidden states, and 1 attention heads. | 6026beafae1183e653f7fb3b99af0f94 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | whisper-base-af-za-V4-Ari This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Google FLEURS dataset. It achieves the following results on the evaluation set: - eval_loss: 1.0084 - eval_wer: 32.0267 - eval_runtime: 152.7461 - eval_samples_per_second: 6.154 - e... | bfc71b515524db92bdd318b4848a5a00 |
creativeml-openrail-m | [] | false | This model is a MPSGraph version of stable difussion 1.5. It runs on apple graph ML model. Works with Creata Ai's Diffusion framework, and it supports iOS, Mac OS and iPad for on Device text-to-image generation. Speed: - Macbook M1,M2: 10-20 seconds/image - iPhone: 90 - 180 seconds - iPad Pro: 30-60 seconds The abo... | d2e6edeb136b4473c7a9b1e44077048f |
apache-2.0 | ['translation'] | false | opus-mt-es-ty * source languages: es * target languages: ty * OPUS readme: [es-ty](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-ty/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | cb5c7b8f122779938b0aa392bccc6f8c |
cc-by-sa-4.0 | ['ainu', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a RoBERTa model pre-trained on Ainu texts (in カタカナ, Roman, and Кириллица) for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [roberta-base-ainu-upos](https://huggingface.co/KoichiYasuoka/roberta-base-ainu-upos). | 341f297e8131b13d76d401b6b4ec1846 |
cc-by-sa-4.0 | ['ainu', 'token-classification', 'pos', 'dependency-parsing'] | false | text = "+text+"\n" v=[(s,e) for s,e in w["offset_mapping"] if s<e] for i,(s,e) in enumerate(v,1): q=self.model.config.id2label[p[i,h[i]]].split("|") u+="\t".join([str(i),text[s:e],"_",q[0],"|".join(q[1:-1]),"_",str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n" return... | ef0a93f9b489d51830c22663352ed21e |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_... | 4c84e768b88b7a24f600fc7e2aab85a6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9042 | 1.0 | 641 | 1.8638 | | 1.8516 | 2.0 | 1282 | 1.8250 | | 1.8034 | 3.0 | 1923 | 1.8095 | | 9fce55aef3d3a0f994fc57f63b9fe987 |
apache-2.0 | ['generated_from_trainer'] | false | fix_punct_uncased_t5_small This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the [NPR utterances dataset](https://www.kaggle.com/datasets/shuyangli94/interview-npr-media-dialog-transcripts?select=utterances.csv). | acd69ed945df1dcf348fb323ae29d48a |
apache-2.0 | ['generated_from_trainer'] | false | Dataset The model was trained on 80k rows from the above dataset consisting of NPR radio transcripts. Commans, periods, and semicolons were removed from the text and then random commas, periods, and semicolons were added. The model was trained to place those three punctuation marks in the correct location. All texts ... | aa195dc32cef7732b75f16171f78a569 |
apache-2.0 | ['generated_from_trainer'] | false | Model description The purpose of this model is to correct the punctuation in a sentence. For example, the phrase "this is, a sentence. with odd punctuation to show off what, the model. can do" gets changed to "this is a sentence with odd punctuation to show off what the model can do." | 543f31ca1f0d69267eb2da98de06bdfb |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 128 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | f33cd7cadff362ce501b8602f48a0313 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.3066 | 1.0 | 600 | 0.4347 | 59.0002 | 54.7692 | 58.7112 | 58.7856 | 16... | f3b5b6bc2753a5e95311f475af3c1d46 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0644 - Precision: 0.9344 - Recall: 0.9500 - F1: 0.9422 - Accuracy: 0.9860 | eb1e87288c9ed19a563e8ac49a1395ef |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0854 | 1.0 | 1756 | 0.0632 | 0.9080 | 0.9352 | 0.9214 | 0.9822 | | 0.0401 | 2.0 |... | 203d4940c94f411e39925920864a03b4 |
apache-2.0 | ['translation'] | false | rus-lit * source group: Russian * target group: Lithuanian * OPUS readme: [rus-lit](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-lit/README.md) * model: transformer-align * source language(s): rus * target language(s): lit * model: transformer-align * pre-processing: normalization + S... | 74b1f7700b52bb90d0d73e68eed26b27 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: rus-lit - source_languages: rus - target_languages: lit - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-lit/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ru', 'lt'] - src_constituents: {'rus'} - tgt_const... | ea372d3df839975c388728b763ebe141 |
cc-by-4.0 | ['generated_from_trainer'] | false | movie-roberta-base-finetuned-movie-p1 This model is a fine-tuned version of [thatdramebaazguy/movie-roberta-base](https://huggingface.co/thatdramebaazguy/movie-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3949 | 658b081752aa48eb1d556625e7f15de8 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.7521 | 1.0 | 108 | 4.7594 | | 4.289 | 2.0 | 216 | 2.8672 | | 2.5416 | 3.0 | 324 | 1.3464 | | 1.2104 | 4.0 | 432 | 0.6174 ... | 831e334d73b4d81f248a42905a65ffb0 |
mit | ['generated_from_trainer'] | false | geocoder_model_xlm_roberta_50 This model is a fine-tuned version of [azamat/geocoder_model_xlm_roberta_50](https://huggingface.co/azamat/geocoder_model_xlm_roberta_50) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 29.6316 - eval_mse: 29.6316 - eval_mae: 2.0573 - eval_r2: 0... | 4620c64e39200028add122f05ad4ead8 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 | 0f12531022a411cff4dc1718bdd50e87 |
other | ['vision'] | false | SegFormer (b1-sized) encoder pre-trained-only SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVla... | d0bf52a4544d29aa4222645ae5a6efed |
other | ['vision'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import SegformerFeatureExtractor, SegformerForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769... | 26033406e835be22a266b84362e0f39b |
apache-2.0 | ['vision', 'image-classification'] | false | Swin Transformer v2 (base-sized model) Swin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this reposi... | 147ae18e4f83c9706fbe590cd3157150 |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, AutoModelForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" i... | 64bed6ac9beb58c15ff71db72ed1ac34 |
apache-2.0 | ['translation'] | false | opus-mt-gv-en * source languages: gv * target languages: en * OPUS readme: [gv-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/gv-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | 08b7e36059c6927fdc2c6650800612ac |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | georgeart Dreambooth model trained by Alexwww with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Please put the prompt: flat, minimal, illustration Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.res... | 12cd0b3e24d0355c112fdfb46670cfba |
openrail | ['translation'] | false | EnViT5 Translation [](https://paperswithcode.com/sota/machine-translation-on-iwslt2015-english-1?p=mtet-multi-domain-translation-for-english) [ on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1607 | 6b925491ca0655da34d894358da7e20f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.8695 | 1.0 | 5540 | 0.9092 | | 0.6594 | 2.0 | 11080 | 0.9148 | | 0.5053 | 3.0 | 16620 | 0.9641 | | 0.3477 | 4.0 | 22160 | 1.1607 ... | ea7554c250f67ca42e0712e1c3cddcd5 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the dota concept trained by Ducco on the Ducco/dota2style dataset. This is a Stable Diffusion model fine-tuned on the dota concept with DreamBooth. It can be used by modifying the `instance_prompt`: **dota style** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisa... | 7967cdd7e78428538680bf99dc9f734f |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-ours-DS This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9899 - Accuracy: 0.725 - Precision: 0.6875 - Recall: 0.6723 - F1: 0.6779 | 06808dceae5840a39b884be2a43a09ae |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.6820964947491663e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 6 | a8380269172425234d92e027ad97edf4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.9962 | 1.99 | 199 | 0.8025 | 0.59 | 0.6055 | 0.5507 | 0.4746 | | 0.6724 | 3.98 |... | 9632d12c47f74ba4aae41e64d2902802 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Low Poly Landscape on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Low Poly Landscape concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of lowpoly_landscape** | f5ef8998ea0a836f70a69e7712274ba2 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model:  | f51cdaa6abc9674d66ad32ed15e33c12 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [](https://discord.gg/9B2Pu2bEvj) [Image Source](https://www.deviantart.com/kautsar211086/art/Long-Time-No-See-457162094) | a68c5481d05ba6ae68d4864c3875ed08 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-large-squadshifts-vanilla-new_wiki-qg` This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: new_wiki) via [`... | 409304742f272b5b7b255ac33c8ddb16 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [facebook/bart-large](https://huggingface.co/facebook/bart-large) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (new_wiki) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://gi... | 8c834def6c98fe9bf36e10893c7cdf4e |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/bart-large-squads... | 2e02a4da3e2b5016d2c9e2a8ec917ecd |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-large-squadshifts-vanilla-new_wiki-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.new_wiki.json) | | Score | Type | Dataset ... | 7b9fb17fc786d79b1a6818774d917a4a |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: new_wiki - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-large - max_length: 512 - max_length_output: 32 - epoc... | 95e044bc27bf07d84baeaae2bd206729 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat](https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0416... | 2f1daeb435bd61cd37fb476002c0f6c2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1296 | 1.0 | 190 | 0.0646 | 0.9774 | | 0.1257 | 2.0 | 380 | 0.0445 | 0.9841 | | 0.1067 | 3.0 | 570 | 0.0416 | 0.... | 2923dc51604776c7a1df065d4dbeea34 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UR dataset. It achieves the following results on the evaluation set: - Loss: 1.2924 - Wer: 0.7201 | a26387267c00dfc965b683df59ad77d2 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - 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_sch... | d4106ddfdff49cee4585a66fb08f3973 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 11.2783 | 4.17 | 100 | 4.6409 | 1.0 | | 3.5578 | 8.33 | 200 | 3.1649 | 1.0 | | 3.1279 | 12.5 | 300 | 3.0335 | 1.0 ... | a38c1d873f778fa50ed46b4affdb2c5d |
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: 0.2182 - Accuracy: 0.9275 - F1: 0.9275 | dbbf63a6d76842006b645d63d7e530a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8403 | 1.0 | 250 | 0.3135 | 0.9065 | 0.9031 | | 0.2525 | 2.0 | 500 | 0.2182 | 0.9275 | 0.9275 | | 45d13628dee3d1ac7c7cfa6c7b944a07 |
cc-by-4.0 | ['question answering'] | false | Model Card of `lmqg/bart-base-tweetqa-qa` This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question answering task on the [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-questio... | 6762901bc954060fb1f4be2081dd723b |
cc-by-4.0 | ['question answering'] | false | Overview - **Language model:** [facebook/bart-base](https://huggingface.co/facebook/bart-base) - **Language:** en - **Training data:** [lmqg/qg_tweetqa](https://huggingface.co/datasets/lmqg/qg_tweetqa) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/as... | 5fa3d1f46874fbb609dfa70a7e894494 |
cc-by-4.0 | ['question answering'] | false | model prediction answers = model.answer_q(list_question="What is a person called is practicing heresy?", list_context=" Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy,... | 1189be8bf3f467721f91330836150495 |
cc-by-4.0 | ['question answering'] | false | Evaluation - ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/lmqg/bart-base-tweetqa-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_tweetqa.default.json) | | Score | Type | Dataset | ... | 45c800709c6f28f762e021c22fa63ea3 |
cc-by-4.0 | ['question answering'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_tweetqa - dataset_name: default - input_types: ['paragraph_question'] - output_types: ['answer'] - prefix_types: None - model: facebook/bart-base - max_length: 512 - max_length_output: 32 - epoch: 3 ... | aa16521d0a1b329cea7e6509fee9ebe8 |
apache-2.0 | ['summarization', 'question-generation'] | false | Introduction This model checkpoint is obtained by fine-tuning the `sshleifer/distilbart-cnn-6-6` summarization checkpoint on the SQuAD dataset. [GitHub Link for training scripts.](https://github.com/darth-c0d3r/bart-question-generation) | e397a9d7734266858129c2b4773b2c96 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5355 - Matthews Correlation: 0.5491 | b938f21478c7ec76e6d09e152833cbbf |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5261 | 1.0 | 535 | 0.5485 | 0.3887 | | 0.3488 | 2.0 | 1070 | 0.4993 | 0.4858 | | 0.2... | 8eed3f50d0b53a421670a6e4314b2192 |
apache-2.0 | ['generated_from_keras_callback'] | false | Rocketknight1/model-card-callback-test-new This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0031 - Train Accuracy: 1.0 - Validation Loss: 0.0000 - Validation Accur... | a91df8b8f6a31b146551327c008a0e5d |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.4647 | 0.6406 | 0.0057 | 1.0 | 0 | | 0.0031 | 1.0 | 0.0000 | 1.0 ... | 82edf2523386009d55936197d8e5529f |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change over the course of training, we provi... | fecf740921e458712ab3881871d36a8d |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-2.8B has not been fine-tuned for down... | baeb45586ef41bde121343cd53f624c6 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-2.8B to produce factually accurate output. This model was trained ... | 6599533780078d5483c2c424a5dad4ef |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Training data [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. Common... | ad5f7acbd3c47e303a6bb7897f87b759 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_stsb_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.2820 - Pearson: 0.0445 - Spearmanr: 0.0342 - Combined Score: 0.0393 | 985005dccf2a285ce47ea7fb9e4ea131 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 5.0491 | 1.0 | 45 | 2.6735 | -0.0094 | -0.0099 | -0.0097 | | 2.2021 | 2.0 | 90 ... | 967f3b54dc2ccbbef16323767b66f111 |
apache-2.0 | ['image-classification', 'pytorch', 'onnx'] | false | Model description The core idea of the author is to distinguish the training architecture (with shortcut connections), from the inference one (a pure highway network). By designing the residual block, the training architecture can be reparametrized into a simple sequence of convolutions and non-linear activations. | 8b73dbdcca6aedc42b0d63248d7a3289 |
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 holocron.models import model_from_hf_hub model = model_from_hf_hub("frgfm/repvgg_a0").eval() img = Ima... | c2afff629b119b09564fd85d37c0199e |
apache-2.0 | ['image-classification', 'pytorch', 'onnx'] | false | Citation Original paper ```bibtex @article{DBLP:journals/corr/abs-2101-03697, author = {Xiaohan Ding and Xiangyu Zhang and Ningning Ma and Jungong Han and Guiguang Ding and Jian Sun}, title = {RepVGG: Making VGG-style ConvNets Grea... | 13ecb7192bda7cd8996f97420d31d473 |
creativeml-openrail-m | ['text-to-image'] | false | sd-bib Dreambooth model trained by tzvc with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/ma... | 7603c6d5073729d87afb906933e75930 |
cc-by-4.0 | [] | false | TeluguBERT TeluguBERT is a Telugu BERT model trained on publicly available Telugu monolingual datasets. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT a... | c6a39cb0da55c0c68e5d39237aabb305 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Hi - Swedish This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3275 - Wer: 19.6849 | ea6149edc930ad9fa2f507294884c22c |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1378 | 1.29 | 1000 | 0.2953 | 21.4165 | | 0.0475 | 2.59 | 2000 | 0.2913 | 20.3275 | | 0.0187 | 3.88 | 3000 | 0.3026 | 19.900... | 3464e570ce910000b342f98bcc8ff55b |
apache-2.0 | ['generated_from_trainer'] | false | Model description This model is fine-tuned on the extractive question answering task -- The Stanford Question Answering Dataset -- [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/). For convenience this model is prepared to be used with the frameworks `PyTorch`, `Tensorflow` and `ONNX`. | 540660e3fe3693f8fa58fbef7db373ab |
apache-2.0 | ['generated_from_trainer'] | false | Intended uses & limitations This model can handle mismatched question-context pairs. Make sure to specify `handle_impossible_answer=True` when using `QuestionAnsweringPipeline`. __Example usage:__ ```python >>> from transformers import AutoModelForQuestionAnswering, AutoTokenizer, QuestionAnsweringPipeline >>> mode... | 27fdc4d770212ffb2a3a9c89a2f641a7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - distributed_type: tpu - num_devices: 8 - total_train_batch_size: 512 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 ... | 1a107b8930afec38bbdb35b46d5f148b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Metric | Value | |:-------------------------|-------------:| | epoch | 3 | | eval_HasAns_exact | 67.5776 | | eval_HasAns_f1 | 74.3594 | | eval_HasAns_total | 5928 | | eval_NoAns_exact | 62.9... | cb9080b5779b0959404e64bab74ae180 |
apache-2.0 | ['generated_from_trainer'] | false | About Us <img src="https://squirro.com/wp-content/themes/squirro/img/squirro_logo.svg" alt="Squirro Logo" width="250"/> Squirro marries data from any source with your intent, and your context to intelligently augment decision-making - right when you need it! An Insight Engine at its core, Squirro works with global ... | 449a362756d8a86e2e0cc56fc12c3ceb |
apache-2.0 | ['generated_from_trainer'] | false | Social media profiles: - Redefining AI Podcast (Spotify): https://open.spotify.com/show/6NPLcv9EyaD2DcNT8v89Kb - Redefining AI Podcast (Apple Podcasts): https://podcasts.apple.com/us/podcast/redefining-ai/id1613934397 - Squirro LinkedIn: https://www.linkedin.com/company/squirroag - Squirro Academy LinkedIn: https://w... | ffc7a23ae8e06a4aa25e07879e2ecef0 |
mit | ['layoutlm', 'pdf'] | false | LayoutLM for Visual Question Answering This is a fine-tuned version of the multi-modal [LayoutLM](https://aka.ms/layoutlm) model for the task of question answering on documents. It has been fine-tuned using both the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) and [DocVQA](https://www.docvqa.org/) datasets. | 3b2426a407242268d196ac127a642570 |
mit | ['layoutlm', 'pdf'] | false | Getting started with the model To run these examples, you must have [PIL](https://pillow.readthedocs.io/en/stable/installation.html), [pytesseract](https://pypi.org/project/pytesseract/), and [PyTorch](https://pytorch.org/get-started/locally/) installed in addition to [transformers](https://huggingface.co/docs/transf... | d90b2304ebcc42dfe81725964965f30d |
mit | ['layoutlm', 'pdf'] | false | {'score': 0.9912159, 'answer': '$1,000,000,000', 'start': 97, 'end': 97} nlp( "https://www.accountingcoach.com/wp-content/uploads/2013/10/income-statement-example@2x.png", "What are the 2020 net sales?" ) | 87c5b99d2ebe40d374a8248d8827eea4 |
mit | ['layoutlm', 'pdf'] | false | 18414](https://github.com/huggingface/transformers/pull/18414), so you'll need to use a recent version of transformers, for example: ```bash pip install git+https://github.com/huggingface/transformers.git@2ef774211733f0acf8d3415f9284c49ef219e991 ``` | 62ad62709ad60e94c1cfa605c9251e6a |
apache-2.0 | ['tapas'] | false | reader-models). It is described in Herzig et al.'s (2021) [paper](https://aclanthology.org/2021.naacl-main.43/) _Open Domain Question Answering over Tables via Dense Retrieval_. This model has 2 versions which can be used differing only in the table scoring head. The default one has an adapted table scoring head in or... | 70d6a325ccc3d01ee74cff68ebf5802f |
apache-2.0 | ['tapas'] | false | In Haystack If you want to use this model for question-answering over tables, you can load it in [Haystack](https://github.com/deepset-ai/haystack/): ```python from haystack.nodes import TableReader table_reader = TableReader(model_name_or_path="deepset/tapas-large-nq-reader") ``` | eddc0b45b11812bfe4befdb4349a89d5 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-wikitextepoch_150 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8929 | e3afb776cd957d713a1e0d828a94b4e4 |
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: 150 | fc55abae7ed65a6bc23843c5feba9fa0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 2.2428 | 1.0 | 1121 | 2.0500 | | 2.1209 | 2.0 | 2242 | 1.9996 | | 2.0665 | 3.0 | 3363 | 1.9501 | | 2.0179 | 4.0 | 4484 | 1... | ed2227d3212a2782ba95af8b862c9c7d |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xlsr-53-espeak-cv-ft-mhr-ntsema-colab This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7728 - Wer: 0.8127 | a582b75b554c05411635ed09b6f24b66 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.8463 | 5.79 | 400 | 1.0428 | 0.9331 | | 1.4576 | 11.59 | 800 | 0.6796 | 0.8495 | | 0.8054 | 17.39 | 1200 | 0.7131 | 0.8227 | |... | d9a10d67ebf0f37e7d27330150794903 |
mit | ['bridgetower'] | false | BridgeTower base model The BridgeTower model was proposed in "BridgeTower: Building Bridges Between Encoders in Vision-Language Representative Learning" by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan. The model was pretrained on English language using masked language modeling (MLM) and... | ac6806dfc89415214a0d0c094721a325 |
mit | ['bridgetower'] | false | Model description The abstract from the paper is the following: Vision-Language (VL) models with the Two-Tower architecture have dominated visual-language representation learning in recent years. Current VL models either use lightweight uni-modal encoders and learn to extract, align and fuse both modalities simultane... | cff190ae64666b52c1052c4e2b9a180b |
mit | ['bridgetower'] | false | Intended uses & limitations(TODO) You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=BridgeTower) to look for fine-tuned versions on a task that interests you. | e87d57a0deb2a0262b90735e4ffc3f19 |
mit | ['bridgetower'] | 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 BridgeTowerProcessor, BridgeTowerModel import requests from PIL import Image url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw... | e13d7622002b4767cbd0d34ae7cc07b8 |
mit | ['bridgetower'] | false | Training data The BridgeTower model was pretrained on four public image-caption datasets: - [Conceptual Captions(CC)](https://ai.google.com/research/ConceptualCaptions/), - [SBU Captions](https://www.cs.rice.edu/~vo9/sbucaptions/), - [MSCOCO Captions](https://arxiv.org/pdf/1504.00325.pdf), - [Visual Genome](https://... | a6ecca79ba40dddb67e7c81b89410b0f |
mit | ['bridgetower'] | false | Pretraining The model was pre-trained for 100k steps on 8 NVIDIA A100 GPUs with a batch size of 4096. The optimizer used was AdamW with a learning rate of 1e-5. No data augmentation was used except for center-crop. The image resolution in pre-training is set to 288 x 288. | e50e29fd54f0fb07a36bcc6257bbe371 |
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