license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['translation'] | false | System Info: - hf_name: vie-fra - source_languages: vie - target_languages: fra - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-fra/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['vi', 'fr'] - src_constituents: {'vie', 'vie_Hani'} ... | 85c15f3926c7738ff842388ea3e90652 |
cc-by-4.0 | ['bert'] | false | bert-sr-small A small-size BERT Language Model with a **shuffle + random** pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to [How does the pre-training objective affect what large language models learn about linguistic properties?](https://a... | a52d2349c899501634d6029ee9936e36 |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'summary', 'booksum', 'long-document', 'long-form'] | false | long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP17 This model is a fine-tuned version of [pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP16](https://huggingface.co/pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP16) on the kmfoda/booksum dataset. | 008a9c6eaf58e1024d0243e2c828ee6c |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'summary', 'booksum', 'long-document', 'long-form'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 64 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_s... | 741c9b0f63f370135490d0a35e150d1e |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_vp-100k_age_teens-10_sixties-0_s232 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using t... | 89d1f5640185c25130d469f62f7bac18 |
mit | ['bert', 'pytorch', 'tsdae'] | false | Introduction Legal_BERTimbau Large is a fine-tuned BERT model based on [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) Large. "BERTimbau Base is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recogniti... | 27b7da55d295884576802fb60de815a6 |
mit | ['bert', 'pytorch', 'tsdae'] | false | Params | | ---------------------------------------- | ---------- | ------- | ------- | |`rufimelo/Legal-BERTimbau-base` |BERT-Base |12 |110M| | `rufimelo/Legal-BERTimbau-large` | BERT-Large | 24 | 335M | | e4c4e06247deef3be00e50265ce8fee6 |
mit | ['bert', 'pytorch', 'tsdae'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("rufimelo/Legal-BERTimbau-large-TSDAE") model = AutoModelForMaskedLM.from_pretrained("rufimelo/Legal-BERTimbau-large-TSDAE") ``` | aa4f6ebb2043ec609e0c858c0ee62a64 |
mit | ['bert', 'pytorch', 'tsdae'] | false | Masked language modeling prediction example ```python from transformers import pipeline from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("rufimelo/Legal-BERTimbau-large-TSDAE") model = AutoModelForMaskedLM.from_pretrained("rufimelo/Legal-BERTimbau-large-TSD... | 74ba74db369dac16eea4f0778c515346 |
mit | ['bert', 'pytorch', 'tsdae'] | false | For BERT embeddings ```python import torch from transformers import AutoModel model = AutoModel.from_pretrained('rufimelo/Legal-BERTimbau-large-TSDAE') input_ids = tokenizer.encode('O advogado apresentou recurso para o juíz', return_tensors='pt') with torch.no_grad(): outs = model(input_ids) encoded = outs[0... | caef72ff527f3dcfecd2d6177a471908 |
mit | ['bert', 'pytorch', 'tsdae'] | false | Citation If you use this work, please cite BERTimbau's work: ```bibtex @inproceedings{souza2020bertimbau, author = {F{\'a}bio Souza and Rodrigo Nogueira and Roberto Lotufo}, title = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese}, booktitle = {9th Brazi... | e93e23adcbeb738c04c49f70f7e51099 |
mit | [] | false | dapSciBERT DapSciBERT is a BERT-like model trained based on the domain adaptive pretraining method ([Gururangan et al.](https://aclanthology.org/2020.acl-main.740/)) for the patent domain. Allenai/scibert_scivocab_uncased is used as base for the training. The training dataset used consists of a corpus of 10,000,000 p... | f1ec6a09f217a6298400b2b4dffeee90 |
mit | ['generated_from_trainer'] | false | poem-gen-spanish-t5-small-v5 This model is a fine-tuned version of [hackathon-pln-es/poem-gen-spanish-t5-small](https://huggingface.co/hackathon-pln-es/poem-gen-spanish-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8881 | 67a67b1e34f2ffe7f38362116224407c |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000125 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 7c4e6d374f4e54840931e6ceb68b5f9d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 2.9366 | 0.73 | 30000 | 2.9656 | | 2.7518 | 1.46 | 60000 | 2.9120 | | 2.6018 | 2.19 | 90000 | 2.8870 | | 2.5262 | 2.93 | 120000 | 2... | e348b308852229b1d288b78d307c25fc |
mit | ['roberta-base', 'roberta-base-epoch_46'] | false | RoBERTa, Intermediate Checkpoint - Epoch 46 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | fa67ebd8b90b1aa5085d7eead9772151 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_One_250v4_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one250v4_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.3389 - Precision: 0.5685 - Recall: 0.4847 - F1: 0.5233 - Accura... | 037299f78ebf11d5a67c2905d5da3168 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 87 | 0.4018 | 0.2797 | 0.1842 | 0.2221 | 0.8514 | | No log | 2.0 |... | 1a09ee7635a65f7b056883c32158eefb |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-sst2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2345 - Accuracy: 0.9140 | 7f977488b9c65882fcf70fdd00b089ce |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.6253 | 0.12 | 500 | 0.3641 | 0.8567 | | 0.3189 | 0.24 | 1000 | 0.2656 | 0.8899 | | 0.2701 | 0.36 | 1500 | 0.3463 ... | f736772342ff6ad379f65e43125a984f |
mit | [] | false | babushork on Stable Diffusion This is the `<babushork>` 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 t... | de182ed81b1bbe4e72df734dac5d98a6 |
apache-2.0 | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | segformer-b0-finetuned-segments-sidewalk This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 0.5679 - Miou: 0.2769 - Macc: 0.3331 - Overall Accuracy: 0.8424 - Per Categor... | 7bcabfa0b91f1465f19eeb772e36469e |
apache-2.0 | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 68207b660badde905464a20c124f3055 |
apache-2.0 | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Miou | Macc | Overall Accuracy | Per Category Iou ... | 2d8355abca867f8da648661135424511 |
apache-2.0 | ['classification'] | false | IDEA-CCNL/Erlangshen-TCBert-1.3B-Sentence-Embedding-Chinese - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) | bf5e24af0e3b0e424acaa8ab02248fc9 |
apache-2.0 | ['classification'] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 句子表征 | 二郎神 Erlangshen | TCBert (sentence representation) | 1.3BM | Chinese | | 18ce55ef5a83712e432eb43c15ea4b90 |
apache-2.0 | ['classification'] | false | Loading models tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-1.3B-Sentence-Embedding-Chinese") model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-1.3B-Sentence-Embedding-Chinese") | cc6e2ed37628f17c727cc72f310a4946 |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-avengers-v1 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5324 - Accuracy: 0.8683 Refer to this [medium article](https://medium... | e854b6ed733d76815f4fef8a7eb93750 |
apache-2.0 | ['generated_from_trainer'] | false | Limitations Training was done on google images for these search terms each representing a class. Iron Man,Captain America,Thor,Spider Man,Docter Strage,Black Panther,Ant Man,Captain Marvel,Hulk,Black Widow,Hawkeye Avengers,Scarlet Witch,Vision Avengers,Bucky Barnes,Falcon Avengers,Loki Therefore it has seen more of i... | 7a14f77ea67de55a2ede2c4448342aa2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8183 | 1.27 | 100 | 1.0134 | 0.8464 | | 0.2234 | 2.53 | 200 | 0.6146 | 0.8495 | | 0.1206 | 3.8 | 300 | 0.5324 | 0.... | caabd96fb9ae78521961926b826aa379 |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.3829 - Rouge1: 0.1966 - Rouge2: 0.0969 - Rougel: 0.1655 - Rougelsum: 0.1657 - Gen Len: 19.0 | f75d6142e8ee8336d96f28f279858f5a |
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 | 248 | 2.5380 | 0.1441 | 0.0519 | 0.1188 | 0.1189 | 19.0 | |... | 8b3c3af80a33443fbf7ef57b46bdbbae |
mit | ['generated_from_trainer'] | false | BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel_PubmedBERT This model is a fine-tuned version of [Sotireas/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel_PubmedBERT](https://huggingface.co/Sotireas/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmo... | ece03d073b9a15a707039732d4f9725a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 21 | 3.8118 | | No log | 2.0 | 42 | 3.5006 | | No log | 3.0 | 63 | 3.1242 | | No log | 4.0 | 84 | 2.9528 ... | c49af33ea3e5845e52ed624eab1408a5 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3182 - Accuracy: 0.8767 - F1: 0.8754 | 33795a66f79d811274aebc35b86e4bce |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'diffusers', 'waifu-diffusion'] | false | Hitokomoru Diffusion V2  A latent diffusion model that has been trained on Japanese Artist artwork, [ヒトこもる/Hitokomoru](https://www.pixiv.net/en/users/30837811). The current model is fine-tuned from [waifu-d... | 647a24db847a102beb15e72bbc1078b0 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'diffusers', 'waifu-diffusion'] | false | Model Details - **Developed by:** Linaqruf - **Model type:** Diffusion-based text-to-image generation model - **Model type:** This is a model that can be used to generate and modify images based on text prompts. - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/b... | 14aa6aac612fc5432862f969b88a899b |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'diffusers', 'waifu-diffusion'] | false | How to Use - Download the `hitokomoru-v2.ckpt` [here](https://huggingface.co/Linaqruf/hitokomoru-diffusion-v2/resolve/main/hitokomoru-v2.ckpt), or download the safetensors version [here](https://huggingface.co/Linaqruf/hitokomoru-diffusion-v2/resolve/main/hitokomoru-v2.safetensors). - This model is fine-tuned from [wa... | 3803d78efb58c1bdb5824b6f7eb52acc |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'diffusers', 'waifu-diffusion'] | false | prompting), an ideal negative prompt to guide the model towards high aesthetic generations would look like: ``` worst quality, low quality, medium quality, deleted, lowres, comic, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, bl... | 9e1f54f45fb3ae657a9edad3f2a7116c |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'diffusers', 'waifu-diffusion'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [M... | 1ec975b0ace9010f23fd4d8aca67fc8a |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'diffusers', 'waifu-diffusion'] | false | Use the DPMSolverMultistepScheduler (DPM-Solver++) scheduler here instead pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) pipe = pipe.to("cuda") prompt = "masterpiece, best quality, high quality, 1girl,... | 2b0f01ce8812ec4c395d6d356e9e5560 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'diffusers', 'waifu-diffusion'] | false | Prompt and settings for Example Images ``` masterpiece, best quality, high quality, 1girl, solo, sitting, confident expression, long blonde hair, blue eyes, formal dress, jewelry, make-up, luxury, close-up, face, upper body. Negative prompt: worst quality, low quality, medium quality, deleted, lowres, comic, bad ana... | 7b66cd673ce8668295461dc6fdbe222c |
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: 0.2146 - Accuracy: 0.9225 - F1: 0.9228 | 29fdb97f0b7e5b6401b99ee8b3d72348 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8233 | 1.0 | 250 | 0.3068 | 0.9025 | 0.8995 | | 0.2394 | 2.0 | 500 | 0.2146 | 0.9225 | 0.9228 | | e16d9671a6adb5ad7e5dd7dc2d6301de |
apache-2.0 | ['generated_from_trainer', 'automatic-speech-recognition', 'NbAiLab/NPSC', 'robust-speech-event', False, 'nb-NO', 'hf-asr-leaderboard'] | false | XLSR-300M-bokmaal This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NBAILAB/NPSC - 16K_MP3_BOKMAAL dataset. It achieves the following results on the evaluation set: - Loss: 0.1635 - Wer: 0.1005 | 7178d2144d7b502dd6e94f6fe4e86b89 |
apache-2.0 | ['generated_from_trainer', 'automatic-speech-recognition', 'NbAiLab/NPSC', 'robust-speech-event', False, 'nb-NO', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.0307 | 0.32 | 500 | 3.0026 | 1.0 | | 2.7865 | 0.64 | 1000 | 2.4849 | 0.9926 | | 0.7522 | 0.95 | 1500 | 0.4567 | 0.359... | a60c18e3acd6c299360ba10daee6ade8 |
other | ['generated_from_trainer'] | false | segformer-b0-scene-parse-150 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset. It achieves the following results on the evaluation set: - Loss: 2.3118 - Mean Iou: 0.0859 - Mean Accuracy: 0.1493 - Overall Accuracy: 0.5430 - Per Category Iou: [0.... | ec3daec83d7e95cf88c5587c19bf77dc |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou ... | 6466b54931d0072a3e3273414b442a92 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Spanish This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | 1c8f619f812603e7a44c9f5a88710d0d |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-es") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-es") ``` | e563f6ea5057c489101876ad4451826b |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-from-scratch-custom-tokenizer-expand-vocab-target-glue-mnli This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola-from-scratch-custom-tokenizer-expand-vocab](https://huggingface.co/muhtasham/tiny-mlm-glue-cola-from-scratch-custom-tokenizer-expand-vocab) on the None dataset. It achieve... | 4f135d78adc7f1808c94e5f93c213ac5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0986 | 0.04 | 500 | 1.0969 | 0.3718 | | 1.0962 | 0.08 | 1000 | 1.0945 | 0.3666 | | 1.0855 | 0.12 | 1500 | 1.0747 | 0.... | 59094d0c7571f58686bdb188eb4386a6 |
mit | ['generated_from_trainer', 'gpt2', 'generation'] | false | Model Card for mpuig/job-experience This model is a fine-tuned version of [GPT-2](https://huggingface.co/gpt2) to generate fake job experience descriptions. While this may not have practical applications in the real world, it served as a valuable learning experience for understanding the process of fine-tuning a lan... | 0816105bd7a1492cf96834ba4d7e8f70 |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/bart-base-squadshifts-amazon-qg` This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: amazon) via [`lmqg`](https://github.... | 35e77bd0b44ebbfe72ca7a58f59dc42d |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (amazon) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://gi... | f9004eaf75009342133e94abfe91e7e0 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/bart-base-squadshifts-amazon... | 2750c9efb4fa31b6872dcd88d2f9ff32 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-base-squadshifts-amazon-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.amazon.json) | | Score | Type | Dataset ... | 3f0f7fe5a2324aa6b714fb256a75529f |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: amazon - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: lmqg/bart-base-squad - max_length: 512 - max_length_output: 32 - epoch... | dcb6a0fd00dfd91016416bce83b9a8f3 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | XLS-R-1B - Hindi This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HI dataset. It achieves the following results on the evaluation set: - Loss: 0.6921 - Wer: 0.3547 | 865582e600b7cd582f655b66ae3d9051 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | f93bdbe00dfc34962fc4b80b2adbc2c7 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.0674 | 2.07 | 400 | 1.3411 | 0.8835 | | 1.324 | 4.15 | 800 | 0.9311 | 0.7142 | | 1.2023 | 6.22 | 1200 | 0.8060 | 0.6170 | |... | 31fa146a495567b2165556c820fd4a0a |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id anuragshas/wav2vec2-xls-r-1b-hi-with-lm --dataset mozilla-foundation/common_voice_8_0 --config hi --split test ``` | e55f07f07044078b262907b555664dbb |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Inference With LM ```python import torch from datasets import load_dataset from transformers import AutoModelForCTC, AutoProcessor import torchaudio.functional as F model_id = "anuragshas/wav2vec2-xls-r-1b-hi-with-lm" sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "hi", split="test", streaming... | 223c55581294120e4c2e843523d746a1 |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-iir-en-finetuned-fa-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-iir-en](https://huggingface.co/Helsinki-NLP/opus-mt-iir-en) on the opus_infopankki dataset. It achieves the following results on the evaluation set: - Loss: 1.0968 - Bleu: 36.687 - Gen Len: 16.039 | 31ae83961eff5e10caf158bc88385490 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - 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 - num_epochs: 30 - mixed_precision_training: Native AMP | 0120492462b49db36b728f343e8be455 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 3.1614 | 1.0 | 1509 | 2.8058 | 12.326 | 16.5467 | | 2.7235 | 2.0 | 3018 | 2.4178 | 15.6912 | 16.6396 | | 2.4839 ... | e6ebdf146df05a1d62ae75079142e90b |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'midjourney', 'artificial', 'artificial-journey', 'journey', 'portrait', 'art', 'diffusion', 'photorealistic'] | false | ARTificialJourney-1.0 768X768 This is an AI model trained on ~100 hand picked 768X768 images targeted to get the best close up portrait pictures, as well as amazing looking landscapes. This model does not handle full-body portraits well due to being trained on more close up images, but it will be improved in the next... | bead86daf4b1bf28170e6c96790a40a7 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'midjourney', 'artificial', 'artificial-journey', 'journey', 'portrait', 'art', 'diffusion', 'photorealistic'] | false | CKPT & Safetensors Download [Download ARTificialJourneyV1-768px.ckpt) (2.9GB)](https://huggingface.co/Kaludi/ARTificialJourney-v1.0-768/blob/main/ARTificialJourneyV1-768px.ckpt) [Download ARTificialJourneyV1-768px.safetensors) (2.9GB)](https://huggingface.co/Kaludi/ARTificialJourney-v1.0-768/blob/main/ARTificialJour... | 4c4590e7ab65d314692336c468d57aff |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'midjourney', 'artificial', 'artificial-journey', 'journey', 'portrait', 'art', 'diffusion', 'photorealistic'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ```python from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler impor... | 212002a6c3e36a85bffbe627a33d50f7 |
mit | ['generated_from_keras_callback'] | false | Amitesh007/text_generation-finetuned-gpt2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.9088 - Validation Loss: 3.6320 - Epoch: 0 | 958401c4f312618efe98e9da1fe50467 |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 | c658dd36091aa10ce750817032eba06e |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1374 - F1: 0.8627 | 19d2adba679500b5550ef04326d43ad6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2596 | 1.0 | 525 | 0.1571 | 0.8302 | | 0.1292 | 2.0 | 1050 | 0.1416 | 0.8455 | | 0.0809 | 3.0 | 1575 | 0.1374 | 0.8627 | ... | 5b696081de7655b8124ce5e071055500 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-stsb-from-scratch-custom-tokenizer-expand-vocab This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.8182 | 10483806e898c6f52b0a136cb3d694cc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.929 | 0.7 | 500 | 6.7432 | | 6.8164 | 1.39 | 1000 | 6.4671 | | 6.5278 | 2.09 | 1500 | 6.3719 | | 6.3088 | 2.78 | 2000 | 6.2202 ... | 9d0d985e379cee67ceb6ff59ef0a1a35 |
apache-2.0 | ['argumentation'] | false | Generate a chain of reasoning from one claim to another This model has the same model parameters as [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), but with an additional soft prompt which has been optimized on the task of generating a sequence of claims (a 'chain of reasoning') that joins one claim... | 9d28c8d16792526cdefefc5175a0b5b5 |
mit | ['summarization'] | false | Model description BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. BART is particularly eff... | c0d481e2f80d7904ce8956f8a1859a01 |
mit | ['summarization'] | false | How to use Here is how to use this model with the [pipeline API](https://huggingface.co/transformers/main_classes/pipelines.html): ```python from transformers import pipeline summarizer = pipeline("summarization", model="ML-unipi/bart-large-tos") ARTICLE = """ New York (CNN)When Liana Barrientos was 23 years old, ... | 28b44997e9a179b49444a078812ca7c2 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-rte-target-glue-mrpc This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-rte](https://huggingface.co/muhtasham/tiny-mlm-glue-rte) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0186 - Accuracy: 0.7328 - F1: 0.8143 | afd589995420be9a5f715cc5d7544187 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5841 | 4.35 | 500 | 0.5459 | 0.7279 | 0.8230 | | 0.4421 | 8.7 | 1000 | 0.5767 | 0.7426 | 0.8309 | | 0.2968 |... | 97c415127e29099dca79a8dcb759a7d0 |
apache-2.0 | [] | false | distilbert-base-en-fr-zh-ja-vi-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the origi... | b5d84abe346794f096870aa82f9b6124 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-fr-zh-ja-vi-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-fr-zh-ja-vi-cased") ``` To generate other smaller versions of multilingual transformers pl... | e639ee10b44bbdc1d206353ac0efa83f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model Details Neural machine translation model for translating from Italic languages (itc) to Baltic languages (bat). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the wor... | 1403ec9bd387c9cdc7503d21918e3a03 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | How to Get Started With the Model A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>lit<< Els gats són complexos individus.", ">>sgs<< No." ] model_name = "pytorch-models/opus-mt-tc-big-itc-bat" tokenizer = MarianTokenizer.from_pretrained(model_name) mo... | a4235bb4d4bdc68e3cc20974d0e1422a |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | no no no no no no no no no no no no no no no no no no no no no ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-itc-bat") print(pipe(">>lit<< Els gats són complexos indiv... | ccfeb58b4839fd580e1ca38dc42d984f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Training - **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) - **Pre-processing**: SentencePiece (spm32k,spm32k) - **Model Type:** transformer-big - **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-b... | c13178cf381d84a3462291df9fdbe4f1 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-bat/opusTCv20210807_transformer-big_2022-07-27.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/it... | 2f93839372607bb93709951f9ca31423 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | ita-lit | tatoeba-test-v2021-08-07 | 0.67640 | 40.9 | 224 | 1321 | | spa-lit | tatoeba-test-v2021-08-07 | 0.68805 | 45.9 | 454 | 2352 | | cat-lav | flores101-devtest | 0.52215 | 21.9 | 1012 | 22092 | | cat-lit | flores101-devtest | 0.52380 | 20.2 | 1012 ... | ec37d38f3da832f424b14028d50867cc |
apache-2.0 | ['generated_from_trainer'] | false | albert-base-v2-finetuned-wnli This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6981 - Accuracy: 0.5634 | f0fdae77a5dc6bd88efc255296145a51 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 10 | 0.6954 | 0.4930 | | No log | 2.0 | 20 | 0.6981 | 0.5634 | | No log | 3.0 | 30 | 0.7036 | 0.... | 3aef8ac7bffecaff8b9ad452511e54fb |
cc-by-4.0 | [] | false | Model description This is the T5-3B model for System 2 as described in our paper Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE, FigLang workshop @ EMNLP 2022 (Arxiv link: https://arxiv.org/abs/2210.16407) System 2: Jointly predicting the type of figurative language Using type of figurativ... | a015db6c1e404ff52544928476478338 |
cc-by-4.0 | [] | false | How to use this model? We provide a quick example of how you can try out System 2 in our paper with just a few lines of code: ``` >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM >>> model = AutoModelForSeq2SeqLM.from_pretrained("allenai/System2_FigLang2022") >>> tokenizer = AutoTokenizer.from_pretra... | 7d96b1ec1c4ab2d3b61a626a3f4ba05c |
cc-by-4.0 | [] | false | Model details This model is a fine-tuned version of [t5-3b](https://huggingface.co/t5-3b). It achieves the following results on the evaluation set: - Loss: 0.6078 - Rouge1: 62.8674 - Rouge2: 45.0585 - Rougel: 57.5618 - Rougelsum: 57.5172 - Gen Len: 50.7558 | 0b20df1f888ec170c18dbb7170a10b81 |
cc-by-4.0 | [] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.8068 | 0.33 | 1000 | 0.7251 | 30.6353 | 25.0792 | 30.619 | 30.6274 | 19... | ebb50cd39cbfc1c7093c63f19eaa383f |
apache-2.0 | ['vision', 'image-classification'] | false | Vision Transformer (base-sized model) Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper [An Image is Worth 16x16 Words: Transform... | 9c218df9a2cdcd52be9e62f973405d2c |
apache-2.0 | ['vision', 'image-classification'] | false | Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 mill... | 48ebaf29914f848646f9fd20e4c13c9b |
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 ViTFeatureExtractor, ViTForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image =... | b3ef7ebc40b15c118c648e5a01fea72d |
apache-2.0 | ['vision', 'image-classification'] | false | model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might c... | f3fd4821a1d6a5fdb553cb341cd15b4f |
apache-2.0 | ['vision', 'image-classification'] | false | Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py). Images are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and nor... | a09739b803340c9a2c48aa0a99c70595 |
mit | ['recsys', 'pytorch', 'sentence_transformers'] | false | Model Details
`paper-rec` goal is to recommend users what scientific papers to read next based on their preferences. This is a test model used to explore Hugging Face Hub capabilities and identify requirements to enable support for recommendation task in the ecosystem.
| be72267743e22a1fca08d327c6d66030 |
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