license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
cc0-1.0 | ['programming', 'gpt2', 'causal-lm'] | false | GPT-CSRC This is a GPT2 774M model trained on the C/C++ code of the top 10,000 most popular packages in Debian, according to the [Debian Popularity Contest](https://popcon.debian.org/). The source files were deduplicated using a process similar to the OpenWebText preprocessing (basically a locality-sensitive hash to ... | a0de71c0fc4ef19a3533be76af0dbd08 |
cc0-1.0 | ['programming', 'gpt2', 'causal-lm'] | false | Usage ``` >>> import torch >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> model = AutoModelForCausalLM.from_pretrained("moyix/csrc_774m") >>> device = torch.device("cuda") >>> model.to(device) >>> tokenizer = AutoTokenizer.from_pretrained("moyix/csrc_774m") >>> prompt = tokenizer.encode('// say ... | 9368a0a641594ec1916aa18a907c9de7 |
apache-2.0 | ['image-classification', 'image-segmentation'] | false | Keras Implementation of Point cloud classification with PointNet This repo contains the trained model of [Point cloud classification with PointNet](https://keras.io/examples/vision/pointnet/). The full credit goes to: [David Griffiths](https://dgriffiths3.github.io/) | af2f676be9d6ced9c8571e795b70c637 |
apache-2.0 | ['image-classification', 'image-segmentation'] | false | Intended uses & limitations - As stated in the paper, PointNet is 3D perception model, applying deep learning to point clouds for object classification and scene semantic segmentation. - PointNet takes raw point cloud data as input, which is typically collected from either a lidar or radar sensor. | 55edf2e3773fc1618b4b5f8a167e7842 |
apache-2.0 | ['generated_from_keras_callback'] | false | vdsouza1/bert-finetuned-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0253 - Validation Loss: 0.0587 - Epoch: 2 | 935d4b7222e37949f886773575ca66d5 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1293 | 0.0559 | 0 | | 0.0407 | 0.0552 | 1 | | 0.0253 | 0.0587 | 2 | | f938cc3b8ec068e932720c0c18ce3da9 |
mit | ['generated_from_trainer'] | false | ClinicalBioBERT This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9404 - Accuracy: 0.77 - Precision: 0.8333 - Recall: 0.8209 - F1: 0.8271 | 031c67ce9c61fac0ce27bdd5ab83a5f5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.693 | 1.0 | 50 | 0.6142 | 0.61 | 0.8182 | 0.5373 | 0.6486 | | 0.5547 | 2.0 |... | f3a92c18fddc98fcd0e90f5d5f1b711e |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-removed-0530 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1269 - Accuracy: 0.8745 - F1: 0.8745 | 10a0df93f9fed4ed3eeca5de73555bd1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | No log | 1.0 | 3180 | 0.5939 | 0.8113 | 0.8113 | | No log | 2.0 | 6360 | 0.6459 | 0.8189 | 0.8183 | | No log ... | 7a05a5dd770fe9a891b954fb298b3745 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Astronauts Dreambooth model trained by JacobPerera with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stabl... | 0fc9a7974aaa63d25a5495a1e71a5856 |
bsd-3-clause | ['generated_from_trainer'] | false | ast-fleurs-langid-dropout-0.2 This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the fleurs dataset. It achieves the following results on the evaluation set: - Loss: 7.3600 - Accuracy: 0.1819 | 31ba232e58e130e06309e7a5f073f572 |
bsd-3-clause | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | aad7719ed62773ae0e7f8eeaf77f7f12 |
bsd-3-clause | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.0251 | 1.0 | 16987 | 6.7973 | 0.1689 | | 0.0007 | 2.0 | 33974 | 7.3461 | 0.1787 | | 0.0 | 3.0 | 50961 | 7.3600 ... | 971021b68dc80bdf07c6a509f7abfa2f |
apache-2.0 | ['generated_from_trainer'] | false | Flan-T5 (small) fine-tuned on OpenAI summarize_from_feedback for summarizing This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the summarize_from_feedback dataset. It achieves the following results on the evaluation set: - Loss: 2.1488 - Rouge1: 27.2966 - Ro... | 392e723c0592abbfff4e317182dc5004 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 | f71ea8d4d41012b2065d175b4792f811 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.2472 | 1.0 | 2902 | 2.1882 | 26.2033 | 8.83 | 21.3673 | 22.7758 | 18... | 7be02bb49ccb70039d85951ed998bcba |
apache-2.0 | ['automatic-speech-recognition', 'th'] | false | exp_w2v2t_th_xls-r_s590 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (th)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input i... | 7519510aeb7e2c9229035dfbbbec0830 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner-trainer 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: 0.0607 - Precision: 0.9392 - Recall: 0.9515 - F1: 0.9453 - Accuracy: 0.9868 | f508f20b086bf81c21d46797baec87c1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0861 | 1.0 | 1756 | 0.0623 | 0.9173 | 0.9310 | 0.9241 | 0.9832 | | 0.0342 | 2.0 |... | 4ab6e230a998421a218bc873bfd7a163 |
apache-2.0 | ['translation'] | false | opus-mt-fr-guw * source languages: fr * target languages: guw * OPUS readme: [fr-guw](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-guw/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | 38352af285dd58086fde8812022a8c0f |
mit | ['generated_from_trainer'] | false | xlm-robereta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1637 - F1: 0.8621 | e8a9119dfe1053460aa7ff95bec34a27 |
mit | ['text-classification'] | false | Multi2ConvAI-Logistics: finetuned Bert for English
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Logistics (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: English (en)
- model ... | 6f94f0bc157b2427124896b3f6097914 |
cc-by-4.0 | [] | false | HindAlBERT HindAlBERT is a Hindi AlBERT model model trained on publicly available Hindi monolingual datasets. [project link] (https://github.com/l3cube-pune/MarathiNLP) More details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] (<a href='htt... | ed64251cc3a9cf028246faaa9155dff5 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-transferLearning-NL2BASH_seqTrain This model is a fine-tuned version of [kevinum/t5-small-finetuned-English-to-BASH](https://huggingface.co/kevinum/t5-small-finetuned-English-to-BASH) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6524 - Bleu: 48.0701 - Gen Len: 8.902... | a9b3e24be8f21b99f8eb2c9258902859 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 36 | 0.6524 | 48.0701 | 8.9028 | | No log | 2.0 | 72 | 0.6524 | 48.0701 | 8.9028 | | No log |... | 06493fb8b98dc76cb44af5ae2299addb |
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: 0.7475 - Matthews Correlation: 0.5570 | 797c339d70771e795b16431f2b4831be |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5251 | 1.0 | 535 | 0.5304 | 0.4272 | | 0.3474 | 2.0 | 1070 | 0.4874 | 0.5136 | | 0.2... | de6ab8b11a839ef0fca870a89f4dce0a |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | 1cryenginebeta Dreambooth model trained by abbiepam with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stab... | 10c316e9da38546c569dc3d4d54efb38 |
apache-2.0 | ['generated_from_trainer'] | false | openai/whisper-base This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6082 - Wer: 16.5259 | 18eab26ec3074f6688a843646e05a19e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2939 | 4.02 | 1000 | 0.3712 | 14.9737 | | 0.1381 | 8.04 | 2000 | 0.4280 | 16.5207 | | 0.0248 | 13.01 | 3000 | 0.5326 | 16.998... | 11cb1a2f7c1d093b2d8b5c2c4e2bb91c |
mit | [] | false | XLNet (large-sized model) XLNet model pre-trained on English language. It was introduced in the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Yang et al. and first released in [this repository](https://github.com/zihangdai/xlnet/). Disclaimer:... | ddc2af75f3b1eadea0740eb1d9fb88db |
mit | [] | false | Model description XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context. Overall, XLNet achie... | 61591b5b805670d532a2e7bd948b7c70 |
mit | [] | false | Intended uses & limitations The model is mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?search=xlnet) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentenc... | 109aef30591c388db9ad8935f2c1a678 |
mit | [] | false | Usage Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import XLNetTokenizer, XLNetModel tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased') model = XLNetModel.from_pretrained('xlnet-large-cased') inputs = tokenizer("Hello, my dog is cute", retur... | b957863c3dcc01117665c85db526d280 |
mit | [] | false | BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1906-08237, author = {Zhilin Yang and Zihang Dai and Yiming Yang and Jaime G. Carbonell and Ruslan Salakhutdinov and Quoc V. Le}, title = {XLNet: Generalized A... | e5f493808f733676699a503a3ba5c885 |
cc-by-sa-4.0 | [] | false | nlp-waseda/gpt2-xl-japanese This is Japanese GPT2 with approximately 1.5B parameters pretrained on Japanese Wikipedia and CC-100 The model architecture of the model are based on [Radford+ 2019](https://paperswithcode.com/paper/language-models-are-unsupervised-multitask). | 45ddf86bc91069847917392969eccea1 |
cc-by-sa-4.0 | [] | false | Intended uses & limitations You can use the raw model for text generation or fine-tune it to a downstream task. Note that the texts should be segmented into words using [Juman++](https://github.com/ku-nlp/jumanpp) in advance. | 22eac83b38d46bba4478a667cda15282 |
cc-by-sa-4.0 | [] | false | How to use You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python from transformers import pipeline, set_seed generator = pipeline('text-generation', model='nlp-waseda/gpt2-xl-japanese') | 052d53c02127cb427b349ef26e00a119 |
cc-by-sa-4.0 | [] | false | generator = pipeline('text-generation', model='nlp-waseda/gpt2-xl-japanese', device=0) set_seed(42) generator("早稲田 大学 で 自然 言語 処理 を", max_length=30, do_sample=True, pad_token_id=2, num_return_sequences=5) [{'generated_text': '早稲田 大学 で 自然 言語 処理 を 勉強 して いる 大学生 です. 自然 言語 処理 や 音声 認識, 機械 学習 等 に 興味 が あり, 特に 画像'}, {'generat... | 5a523de93a7e452bd22bf3b6ef0c1afe |
cc-by-sa-4.0 | [] | false | Preprocessing The texts are normalized using [neologdn](https://github.com/ikegami-yukino/neologdn), segmented into words using [Juman++](https://github.com/ku-nlp/jumanpp), and tokenized by [BPE](https://huggingface.co/docs/tokenizers/api/models | e7dfbc9cf5b383db39eefdd37cb0d9c5 |
cc-by-sa-4.0 | [] | false | Acknowledgments This work was supported by Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (JHPCN) through General Collaboration Project no. jh221004, "Developing a Platform for Constructing and Sharing of Large-Scale Japanese Language Models". For training models, we used t... | e8fa13d72039be277a9184269a7bd973 |
mit | ['generated_from_trainer'] | false | CR_XLNet_5E This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6034 - Accuracy: 0.9067 | 1428c6393f86694f83b8a132b0b70e15 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5384 | 0.33 | 50 | 0.4165 | 0.8533 | | 0.3633 | 0.66 | 100 | 0.3059 | 0.8867 | | 0.2642 | 0.99 | 150 | 0.2582 | 0.... | 758d2d138e13f942236dcabafe8f6823 |
apache-2.0 | ['generated_from_trainer'] | false | small This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.0998 - Rouge1: 33.2675 - Rouge2: 11.0862 - Rougel: 26.1709 - Rougelsum: 26.1668 - Gen Len: 28.0123 | 16d37b8d73bc75da6bb856fe6c7fbf63 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_... | daa3c5306371c8ca1ace6e0677c7e001 |
apache-2.0 | ['generated_from_keras_callback'] | false | syp1229/koelectra-base-v3-generator-finetuned-koidiom-epoch5 This model is a fine-tuned version of [monologg/koelectra-base-v3-generator](https://huggingface.co/monologg/koelectra-base-v3-generator) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.1280 - Validation Loss:... | 607cf2464b7580ecf5d2c59b1bb3c2e9 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.4450 | 2.1108 | 0 | | 2.2462 | 1.9578 | 1 | | 2.1990 | 1.9394 | 2 | | 2.1306 | 1.9433 | 3 | | 2.1280 | 1.8541 | 4 | | 496e7fa75605e616cd42a22502d9b67f |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 060fdb8b231b980c67b88a00fb8dd644aebbb1c0 pip install -e . cd egs2/librispeech_100/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model pyf98/librispeech_100h_conformer ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 4532ee932720d83571ca2061b9aacd7e |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Mon Feb 7 21:28:00 EST 2022` - python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]` - espnet version: `espnet 0.10.6a1` - pytorch version: `pytorch 1.10.1` - Git hash: `060fdb8b231b980c67b88a00fb8dd644aebbb1c0` - Commit date: `Mon Feb 7 21:26:51 2022 -0500` | ab21c9965f63b068d3381f919e50c7d2 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |beam1_ctc0.3/dev_clean|2703|54402|93.6|5.3|1.1|1.5|8.0|58.5| |beam1_ctc0.3/dev_other|2864|50948|83.7|14.3|2.0|3.2|19.5|81.2| |beam1_ctc0.3/test_clean|2620|52576|93.3|5.6|1.1|1.7|8.4|59.4| |beam1_ctc0.3/test_other|2939|52343|83.5|1... | 753fa547fadd3aae384b9d5bffc81b73 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |beam1_ctc0.3/dev_clean|2703|288456|97.4|1.2|1.4|1.4|4.0|58.5| |beam1_ctc0.3/dev_other|2864|265951|92.5|4.5|3.0|3.2|10.7|81.2| |beam1_ctc0.3/test_clean|2620|281530|97.3|1.2|1.5|1.5|4.2|59.4| |beam1_ctc0.3/test_other|2939|272758|92.... | 38f8e31d29b77411be84c9b16d45954d |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |beam1_ctc0.3/dev_clean|2703|69558|91.0|5.5|3.5|1.4|10.4|58.5| |beam1_ctc0.3/dev_other|2864|64524|80.2|14.7|5.1|4.2|24.0|81.2| |beam1_ctc0.3/test_clean|2620|66983|91.0|5.6|3.4|1.6|10.6|59.4| |beam1_ctc0.3/test_other|2939|66650|80.0... | 764f911ff992bf4b3439b1b6c33c5e10 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_asr_conformer_win400_hop160_ctc0.3_lr2e-3_warmup15k_timemask5_amp_no-deterministic.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_conformer_win400_hop160_ctc0.3_lr2e-3_warmup15k_timemask5_amp_no-... | 46a7a0acf8a3e964ec4219f20cc3e240 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_data_aug_sst2_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.5172 - Accuracy: 0.7867 | 22ae3bf128d6d13aa04cbfee1aec677f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3529 | 1.0 | 8748 | 0.5172 | 0.7867 | | 0.2729 | 2.0 | 17496 | 0.5752 | 0.7695 | | 0.2317 | 3.0 | 26244 | 0.6663 ... | 65ac147c0065a87b7f7c696691fa80b9 |
apache-2.0 | ['Recommendation'] | false | MCTI Recommendation Task (uncased) DRAFT Disclaimer: The Brazilian Ministry of Science, Technology, and Innovation (MCTI) has partially supported this project. The model [NLP MCTI Recommendation Multi](https://huggingface.co/spaces/unb-lamfo-nlp-mcti/nlp-mcti-lda-recommender) is part of the project [Research Financi... | afde111f3ad76211e16e68c3bafc9b32 |
apache-2.0 | ['Recommendation'] | false | According to the abstract, Current model card disposes model's description and it's classes. Also, inteded uses are described along with a "how to use" section, exposing necessary conditions for the data used. Further in the card, data and it's limitation and bias were discussed. Tables along the page supports the in... | bbd7af6254a339975e84907fad4047cd |
apache-2.0 | ['Recommendation'] | false | Model description The surprise library provides 11 classifier models that try to predict the classification of training data based on several different collaborative-filtering techniques. The models provided with a brief explanation in English are mentioned below, for more information please refer to the package [doc... | 2b765ce57f0a9a5b3a5bbb3ef12907e9 |
apache-2.0 | ['Recommendation'] | false | Intended uses You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://www.google.com) to look for fine-tuned versions of a task that interests you. Note that this model is primarily aimed at b... | 64176614a9a193d578f486b6707c6ee2 |
apache-2.0 | ['Recommendation'] | false | How to use The datasets for collaborative filtering must be: - The dataframe containing the ratings. - It must have three columns, corresponding to the user (raw) ids, the item (raw) ids, and the ratings, in this order. ```python >>> import pandas as pd >>> import numpy as np class Data:... | 0d0b199a3db4d1b9466cfc376c08cbc8 |
apache-2.0 | ['Recommendation'] | false | opo = opo.iloc[np.where(opo['opo_brazil']=='Y')] try: lda_model = gensim.models.ldamodel.LdaModel.load(f'models/lda_model{n_users}.model') except: import generate_users generate_users.gen_model(n_users) lda_model = gensim.models.ldamodel.LdaModel... | c3a16170470ac8789209d256bad57037 |
apache-2.0 | ['Recommendation'] | false | Limitations and bias In this model we have faced some obstacles that we had overcome, but some of those, by the nature of the project, couldn't be totally solved. Databases containing profiles of possible users of the planned prototype are not available. For this reason, it was necessary to carry out simulations in ... | 175c2d9c0240e07256797e6298c4efd6 |
apache-2.0 | ['Recommendation'] | false | Checkpoints - Example ```python data=Data() data.show_available_databases() data.read_data('ml_100k') method=Method(data.df) method.show_methods() method.run('surprise.KNNWithMeans') predictions_df=method.predictions_df evaluator=Evaluator(predictions_df) evaluator.show_evaluators() evaluator.run('surprise.mse') `... | f37db1651acef9378a045ea5c0669215 |
apache-2.0 | ['Recommendation'] | false | Codigo para reativar os prints ``` - Usage Example In this section it will be explained how the recommendation is made for the user. ```python import gradio as gr import random import pandas as pd opo = pd.read_csv('oportunidades_results.csv', lineterminator='\n') | d13e7ee39890a936dbd6adba64e09d5b |
apache-2.0 | ['Recommendation'] | false | opo = opo.iloc[np.where(opo['opo_brazil']=='Y')] simulation = pd.read_csv('simulation2.csv') userID = max(simulation['userID']) + 1 This function, creates the string that it will be displayed to the user on the app, showing the opportunities title, link and the resume. def build_display_text(opo_n): title ... | 2f6168bf4538cfd9badc81612ee9eeab |
apache-2.0 | ['Recommendation'] | false | LDA-GENERATED DATASET ranking ``` | | RMSE | MSE | MAE | FCP | |-----------------|-----------|-----------|-----------|-----------| | NormalPredictor | 1.820737 | 3.315084 | 1.475522 | 0.514134 | | BaselineOnly | 1.072843 | 1.150992 | 0.890233 | 0.556560 | | KNNBasic ... | 0dfeb6c6d139475778b98cf49bc7b532 |
apache-2.0 | ['Recommendation'] | false | BENCHMARK DATASET uniform ``` | | RMSE | MSE | MAE | FCP | |-----------------|-----------|-----------|-----------|-----------| | NormalPredictor | 1.508925 | 2.276854 | 1.226758 | 0.503723 | | BaselineOnly | 1.153331 | 1.330172 | 1.022732 | 0.506818 | | KNNBasic ... | 2687c00f21f248ea2de5531a54582171 |
apache-2.0 | ['Recommendation'] | false | BibTeX entry and citation info ```bibtex @unpublished{recommend22, author ={Jo\~{a}o Gabriel de Moraes Souza. and Daniel Oliveira Cajueiro. and Johnathan de O. Milagres. and Vin\´{i}cius de Oliveira Watanabe. and V\´{i}tor Bandeira Borges. and Victor Rafael Celestino.}, title ={A comprehensive review of r... | 43b96fc77f18b644dd60eca20bf37b1f |
apache-2.0 | ['generated_from_trainer'] | false | Article_250v0_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article250v0_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2589 - Precision: 0.6609 - Recall: 0.6239 - F1: 0.6419 - Accuracy: 0.... | 855d35fb77bc2575afccd02791ce88e9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 112 | 0.2475 | 0.5938 | 0.5559 | 0.5742 | 0.9180 | | No log | 2.0 |... | d9f0b581256221e8a0acc1eb1cf31c93 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | MultiBERTs Seed 1 Checkpoint 1200k (uncased) Seed 1 intermediate checkpoint 1200k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/g... | f567bf518579f7b03a8f0c4fe5dbcf1e |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-1-1200k') model = BertModel.from_pretrained("multiberts-seed-1-1200k") text = "Replace me by any text you'd lik... | 0921a67a792f940c1a76fc92066c1d78 |
apache-2.0 | ['generated_from_keras_callback'] | false | market_positivity This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4959 - Train Sparse Categorical Accuracy: 0.8060 - Validation Loss: 0.4484 - Validat... | 884f854ff2344f8af0fdf83ca640f1a6 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch | |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:| | 0.6595 | 0.7184 | 0.5732 ... | 6db0ee00eba358a67467b12c4dd86f32 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | wedadams_bkdbj Dreambooth model trained by tftgregrge with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-st... | df3cf5029ab2c974c06cb908ba7ddd06 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | MultiBERTs Seed 2 Checkpoint 1900k (uncased) Seed 2 intermediate checkpoint 1900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/g... | a03a2838128ee755ab736541cf46d275 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-2-1900k') model = BertModel.from_pretrained("multiberts-seed-2-1900k") text = "Replace me by any text you'd lik... | 68668cdcf51a1f3b5a1408228c7f7b97 |
apache-2.0 | ['translation'] | false | opus-mt-de-pl * source languages: de * target languages: pl * OPUS readme: [de-pl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-pl/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | 0e7927d8a6ecf9b250e57ca71713ecc8 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0329 - Rouge1: 16.3034 - Rouge2: 7.8192 - Rougel: 16.0316 - Rougelsum: 15.9173 | fe9392293ce5080101796409d82e4f0f |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 7.0891 | 1.0 | 1209 | 3.2989 | 13.8686 | 6.1132 | 13.3657 | 13.3454 | | 3.9283 | 2.0 |... | a2496e86e902b67c8d6efc499fd92131 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3826 - Accuracy: 0.4865 | 9bc54ba55dbcef28577dac024a836d52 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.9 | 7 | 1.4323 | 0.4865 | | 1.5843 | 1.9 | 14 | 1.3999 | 0.4865 | | 1.5007 | 2.9 | 21 | 1.3826 | 0.... | a65375683a98d81e8358387d35faf49a |
mit | [] | false | Sherhook Painting on Stable Diffusion This is the `<sherhook>` 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... | 7ea61a1214df5762269c150225a8eabe |
mit | ['exbert', 'authorship-identification', 'fire2020', 'pan2020', 'ai-soco'] | false | Model description From scratch pre-trained RoBERTa model with 1 layers and 96 attention heads using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which consists of C++ codes crawled from CodeForces website. | 4fb48bdd46422561935100fd9bbaf0fc |
mit | ['exbert', 'authorship-identification', 'fire2020', 'pan2020', 'ai-soco'] | false | BibTeX entry and citation info ```bibtex @inproceedings{ai-soco-2020-fire, title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}", author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and ... | e7f3ae3b033e361d807834db01e9cb44 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Stable Diffusion v1-4 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [๐ค—'s Stable Diffusion with ๐งจDiffusers blog](https://huggingface.co/blog/... | b3b0abb1543881b93ba2f158a6c8bbd7 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to โ€A red cube on top of a blue sphereโ€ - Faces and people in general may not be g... | c5d54bb3405583988b90a4fa792a204b |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr 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: 0.1059 - F1: 0.9275 | 939348aed3251545a416170048a477c3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5416 | 1.0 | 191 | 0.2322 | 0.8378 | | 0.2614 | 2.0 | 382 | 0.1544 | 0.8866 | | 0.1758 | 3.0 | 573 | 0.1059 | 0.9275 | ... | 59eeba3f28d6bfe96afe931e3c546b7e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Assamese This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 as dataset. It achieves the following results on the evaluation set: - Loss: 0.6033 - Wer: 35.4990 | dbffac5f2841300d30a5a8b6c3be6447 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - 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: 40 - training_steps: 400 - mixed_precisio... | 207eea87e766174830167277e417e82a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 1.0676 | 3.01 | 50 | 0.6487 | 62.5338 | | 0.2252 | 6.03 | 100 | 0.3487 | 36.4916 | | 0.0787 | 9.04 | 150 | 0.3934 | 35.643... | 0d39d16fb5565dfad3e12ca62562acd7 |
apache-2.0 | ['generated_from_trainer'] | false | M6_MLM This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0237 | faa5cbe0906d7e81d3aafe758530bef1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4015 | 1.0 | 25 | 2.1511 | | 2.2207 | 2.0 | 50 | 2.1268 | | 2.168 | 3.0 | 75 | 2.0796 | | 05567e385cdc5b246c20b6ada3311c67 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001372 - train_batch_size: 1 - eval_batch_size: 8 - seed: 3313214263 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 | a4ac0e33bf7fe913619810977315fb0b |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-adult-child-cls 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: 0.1713 - Accuracy: 0.9460 - F1: 0.9509 | 9d6fbc51c912615962e0d54b2a158c49 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.323 | 1.0 | 96 | 0.2699 | 0.9026 | 0.9085 | | 0.2003 | 2.0 | 192 | 0.2005 | 0.9234 | 0.9300 | | 0.1808 |... | 1cde9f80d9d99bd68b8d82047e5857fa |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_logit_kd_mrpc_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5534 - Accuracy: 0.6838 - F1: 0.8122 - Combined Score: 0.748... | 464710437c37600cd650127ebab16c80 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6399 | 1.0 | 29 | 0.5562 | 0.6838 | 0.8122 | 0.7480 | | 0.6101 | 2.0 | 58 | 0.55... | 9bb6c4c518f88bfcc4f492e3c428728e |
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