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 | ['automatic-speech-recognition', 'ja'] | false | exp_w2v2t_ja_unispeech-ml_s295 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using... | a86605f9a8f9a896aaa9587689e73a8c |
apache-2.0 | ['generated_from_trainer'] | false | M8_MLM This model is a fine-tuned version of [sentence-transformers/paraphrase-albert-small-v2](https://huggingface.co/sentence-transformers/paraphrase-albert-small-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 8.9140 | be318f3b361785d1f5acac93aca7dfca |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 9.5021 | 1.0 | 25 | 9.1463 | | 9.0507 | 2.0 | 50 | 8.9504 | | 8.9528 | 3.0 | 75 | 8.9148 | | 17566abc8e19d072751a91994e6e85bc |
apache-2.0 | ['minds14', 'google/xtreme_s', 'generated_from_trainer'] | false | xtreme_s_xlsr_300m_mt5-small_minds14.en-US This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - MINDS14.EN-US dataset. It achieves the following results on the evaluation set: - Loss: 4.7321 - F1: 0.0154 - Accuracy: 0.0638 | 209200e4512ac2d5735afa5953305253 |
apache-2.0 | ['minds14', 'google/xtreme_s', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 64 - 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_sch... | 0ece639e0ba2c25fd2951d86233e50df |
apache-2.0 | ['minds14', 'google/xtreme_s', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| | 2.6067 | 3.95 | 20 | 2.6501 | 0.0112 | 0.0851 | | 2.5614 | 7.95 | 40 | 2.8018 | 0.0133 | 0.0603 | | 2.2836 |... | caac7a3bfb54e308b981a52013429a4d |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | jenna-ortega-wednesday Dreambooth model trained by anonononimuss 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/TheLast... | bf717dc3d17d1429d73917104866d0d9 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr 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: 0.1619 - F1: 0.8599 | 489c29bcac449e51c39b0be42f9e47d2 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2851 | 1.0 | 715 | 0.1792 | 0.8239 | | 0.149 | 2.0 | 1430 | 0.1675 | 0.8401 | | 0.0955 | 3.0 | 2145 | 0.1619 | 0.8599 | ... | b778c1f75a4fe916b1fee00bf11f2c75 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Rousseau-Style Dreambooth model trained by Lyith 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-stable-... | dbeee2f33ebf7cca53c683393dbbfaf0 |
mit | [] | false | Модель rubert-base-cased от Deeppavlov. Обучена на датасете из предложений. В качестве фактов использовались предложения из Википедии, а в качестве негативных - худлит и новости Датасет: [Den4ikAI/fact_detection](https://huggingface.co/datasets/Den4ikAI/fact_detection) Простейший код инференса: ```python import tor... | 8161af3fbc40d234a2aaf8159b98c919 |
apache-2.0 | ['generated_from_trainer'] | false | bigbird-dialogue-score This model is a fine-tuned version of [google/bigbird-roberta-large](https://huggingface.co/google/bigbird-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2129 - eval_f1: 0.9290 - eval_precision: 0.9173 - eval_recall: 0.9410 - eval_r... | 2d604b1de121c7cb6f1e734091fe9260 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_e... | 980c0d79a6fd38d835a961543ffc6c56 |
apache-2.0 | ['summarization_t5_xsum', 'generated_from_trainer'] | false | bart-base-finetuned-xsum This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.1755 - Rouge1: 34.6293 - Rouge2: 13.4749 - Rougel: 28.2616 - Rougelsum: 28.2553 | ae20df19341c0db49d599a08f039076d |
apache-2.0 | ['summarization_t5_xsum', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | f9f75a0f297ae9830b92a1ad536a1091 |
apache-2.0 | ['summarization_t5_xsum', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 2.4765 | 1.0 | 1000 | 2.0873 | 33.9596 | 12.722 | 27.4135 | 27.4062 | | 1.9854 | 2.0 ... | 0eb893606a439b8ef4bd2be9632a4528 |
apache-2.0 | ['t5'] | false | How to use ```python from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained("KETI-AIR/ke-t5-small-newslike") tokenizer = AutoTokenizer.from_pretrained("KETI-AIR/ke-t5-small-newslike") ``` | 7ed642dde86083970e387a49d2ce791d |
mit | ['generated_from_trainer'] | false | predict-perception-bert-focus-object This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2271 - Rmse: 0.5965 - Rmse Focus::a Su un oggetto: 0.5965 -... | 01c8ccf5fbf7347963c3a8d4d6f76113 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Focus::a Su un oggetto | Mae | Mae Focus::a Su un oggetto | R2 | R2 Focus::a Su un oggetto | Cos | Pair | Rank | Neighbors | Rsa | |:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:-----... | 7ebd9ed49abd238af53c043d5e6f615f |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-mrpc-custom-tokenizer-target-glue-cola This model is a fine-tuned version of [muhtasham/small-mlm-glue-mrpc-custom-tokenizer](https://huggingface.co/muhtasham/small-mlm-glue-mrpc-custom-tokenizer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4117 - Matthews Co... | 5b535155a6aaf17cb30cc8a4072d8a2d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6058 | 1.87 | 500 | 0.6270 | 0.0464 | | 0.5459 | 3.73 | 1000 | 0.6810 | 0.0510 | | 0.4... | 556d4b912be0214d4048c7e23014b9fa |
apache-2.0 | ['generated_from_keras_callback'] | false | YSKartal/SSCI-SciBERT-e2-finetuned-svident This model is a fine-tuned version of [KM4STfulltext/SSCI-SciBERT-e2](https://huggingface.co/KM4STfulltext/SSCI-SciBERT-e2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1916 - Validation Loss: 0.4552 - Train F1: 0.7665 - Epo... | 7f6e7568c1026b41f30ff27481169b1c |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 351, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_... | 38d3325cc7b2812e7800aae371d84608 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train F1 | Epoch | |:----------:|:---------------:|:--------:|:-----:| | 0.1946 | 0.4552 | 0.7665 | 0 | | 0.1944 | 0.4552 | 0.7665 | 1 | | 0.1928 | 0.4552 | 0.7665 | 2 | | 0.1963 | 0.4552 | 0.7665 ... | 759ac21e2c6d6fdd3c194bcf74510dda |
apache-2.0 | ['Vocoder', 'HiFIGAN', 'text-to-speech', 'TTS', 'speech-synthesis', 'speechbrain'] | false | Vocoder with HiFIGAN trained on LJSpeech This repository provides all the necessary tools for using a [ALLFA Public](https://github.com/getalp/ALFFA_PUBLIC/tree/master/ASR/SWAHILI). The pre-trained model takes in input a spectrogram and produces a waveform in output. Typically, a vocoder is used after a TTS model t... | e95ead26f023c9ee8c6b09f85b9c3382 |
apache-2.0 | ['Vocoder', 'HiFIGAN', 'text-to-speech', 'TTS', 'speech-synthesis', 'speechbrain'] | false | Using the Vocoder ```python import torch from speechbrain.pretrained import HIFIGAN hifi_gan = HIFIGAN.from_hparams(source="aioxlabs/hifigan-swahili", savedir="tmpdir") mel_specs = torch.rand(2, 80,298) waveforms = hifi_gan.decode_batch(mel_specs) ``` | 2dbac52bc9b2ba429262eed7e9459536 |
apache-2.0 | ['generated_from_trainer'] | false | gpt-neo-125M-finetuned-pgt This model is a fine-tuned version of [pritoms/gpt-neo-125M-finetuned-pgt](https://huggingface.co/pritoms/gpt-neo-125M-finetuned-pgt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6026 | 4cb0f237701f6cf165968d271e1e8b88 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 26 | 1.5947 | | No log | 2.0 | 52 | 1.5963 | | No log | 3.0 | 78 | 1.6026 | | 16c6833cf6e62c7ea290d012fba2e2eb |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6574 - Wer: 0.5652 | 54dc6c56c6cd4484346508a6d0c0f228 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 10 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 - mixed_precision_t... | 723d30b02965d819f31fe8d999cc9363 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.6258 | 8.77 | 500 | 3.1693 | 1.0 | | 1.4137 | 17.54 | 1000 | 0.6574 | 0.5652 | | 0e7a6635b09ef31ab5b46d314dfe32f1 |
apache-2.0 | ['generated_from_trainer'] | false | english-filipino-wav2vec2-l-xls-r-test-03 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the filipino_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.6932 - Wer: 0.3676 | 411dd2982dd46707bf20853b51fd76ae |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - 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_sched... | a1fd14feeaac2f0ff05433d125df8dd5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.3398 | 2.09 | 400 | 0.5733 | 0.6166 | | 0.5087 | 4.19 | 800 | 0.5210 | 0.4775 | | 0.344 | 6.28 | 1200 | 0.5284 | 0.5008 | |... | 9ff7b2f0359887b04a46af8cf4ade52a |
apache-2.0 | ['generated_from_trainer'] | false | demo_emotion_1234567 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9818 - F1: 0.7348 | c8497b0e41bdb8a90f3aec3a877272d8 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.551070618629693e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | f5f5f1c76accb2a855544679d34a7a32 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 204 | 0.7431 | 0.6530 | | No log | 2.0 | 408 | 0.6943 | 0.7333 | | 0.5176 | 3.0 | 612 | 0.8456 | 0.7326 | |... | 2fa2b70586c6beed73376300892765dc |
apache-2.0 | ['dutch', 't5', 't5x', 'ul2', 'seq2seq'] | false | ul2-base-dutch for Dutch Pretrained T5 model on Dutch using a UL2 (Mixture-of-Denoisers) objective. The T5 model was introduced in [this paper](https://arxiv.org/abs/1910.10683) and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer). The UL2 objective was introduced in... | 966188493ab8e56be659d7430731ef9d |
apache-2.0 | ['dutch', 't5', 't5x', 'ul2', 'seq2seq'] | false | Model description T5 is an encoder-decoder model and treats all NLP problems in a text-to-text format. `ul2-base-dutch` T5 is a transformers model pretrained on a very large corpus of Dutch data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way... | 1b87d64b2cdda28578d8b8f55902505b |
apache-2.0 | ['dutch', 't5', 't5x', 'ul2', 'seq2seq'] | false | How to use Here is how to use this model in PyTorch: ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("yhavinga/ul2-base-dutch", use_fast=False) model = T5ForConditionalGeneration.from_pretrained("yhavinga/ul2-base-dutch") ``` and in Flax: ```pytho... | 46d532c6aeeef1f2405a0fcc1d21a9f6 |
apache-2.0 | ['dutch', 't5', 't5x', 'ul2', 'seq2seq'] | false | Training data The `ul2-base-dutch` T5 model was pre-trained simultaneously on a combination of several datasets, including the full version of the "mc4_nl_cleaned" dataset, which is a cleaned version of Common Crawl's web crawl corpus, Dutch books, the Dutch subset of Wikipedia (2022-03-20), and a subset of "mc4_nl_c... | 299a8960456355a45dd2ab12db53f579 |
apache-2.0 | ['dutch', 't5', 't5x', 'ul2', 'seq2seq'] | false | Preprocessing The ul2-base-dutch T5 model uses a SentencePiece unigram tokenizer with a vocabulary of 32,000 tokens. The tokenizer includes the special tokens `<pad>`, `</s>`, `<unk>`, known from the original T5 paper, `[NLU]`, `[NLG]` and `[S2S]` for the MoD pre-training, and `<n>` for newline. During pre-training ... | cb6208469aed4e9f0389b6b8d9ec4397 |
mit | [] | false | Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> このモデルはrinna/japanese-gpt-1bをベースモデルとして、 コンテキストからの抽出型QAと、解答を新たなコンテキストでリファインするための学習を行ったモデルです。 gpt-index(v0.2.5)で利用することを前提に学習をしており、通常のQAタスクで使用することは想定していません。 利用例はこのリポジトリを参照してください。 https://github.com/oshizo/gpt_index_japanese_trial | 53f8514113578f97a8be6f2ee1c5617f |
mit | [] | false | Model Details モデルは2種類のpromptテンプレートに対してQA応答するように訓練されています。 ```python DEFAULT_PROMPT = """ 文脈情報は以下です。 --- {context_str} --- 事前知識ではなく、文脈情報を参考に質問に答えてください。：{query_str} """ ``` ```python REFINE_PROMPT = """ 質問は以下です。：{query_str} すでに答えの候補があります。：{existing_answer} 必要な場合のみ、以下の文脈情報を使ってこの答えを改良することができます。 --- {context_msg} --- この文... | 3ff229256051eb30f04b55ca19f5b851 |
mit | [] | false | Training Details JGLUE/JSQuADとJaQuADを用いて、コンテキストからの抽出型QAと、解答を新たなコンテキストでリファインするための学習を行いました。 学習スクリプトについてはこのリポジトリを参照してください。 https://github.com/oshizo/gpt_index_japanese_trial Google Colab Pro A100 で約3.5時間、9.9kステップ学習しました。 | 19ee85474c2a621010c017ff559bd01e |
mit | [] | false | Model desciption This is a fine-tuned SpaceSciBERT model, for a Concept Recognition task, from the SpaceTransformers model family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp. The [fine-tuning](https://github.com/strath-ace/smart-nlp/blob/master/Sp... | 37a132a457fcf10b13d25455a72bdfb8 |
apache-2.0 | ['generated_from_keras_callback'] | false | Qiliang/distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9665 - Train End Logits Accuracy: 0.7328 - Train Start Logits ... | 0e99795f6dc65c7da4d4a9135f10b0e7 |
apache-2.0 | ['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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | aa7d04496d5309ae7dd23f2426b85bee |
apache-2.0 | ['generated_from_trainer'] | false | hate_trained_31415 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.8568 - F1: 0.7729 | a47622119cbc890c6787b2e4663de43d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.7272339744854407e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 31415 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 99bd8d40ffb3c712daab09c87e9018d0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.482 | 1.0 | 563 | 0.4973 | 0.7672 | | 0.3316 | 2.0 | 1126 | 0.4931 | 0.7794 | | 0.2308 | 3.0 | 1689 | 0.7073 | 0.7593 | |... | f4a9d871504068b83f4ee5f824dc66e7 |
apache-2.0 | ['generated_from_trainer'] | false | mt5-base-finetuned-xsum-data_prep_2021_12_26___t404_2980.csv___topic_text_google_mt5_base This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 0.8441 - Rouge2: 0.0894 - Roug... | 3d7bac02caf14fa827c59d256ceff1fb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.0 | 1.0 | 89332 | nan | 0.8441 | 0.0894 | 0.8428 | 0.844 | 6.338 ... | f5a01b1679a5110c58d4a555741ff2c9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_4_binary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5144 - F1: 0.8245 | 82a08c2675bae439710445df4470dbb4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.3756 | 0.8175 | | 0.3977 | 2.0 | 578 | 0.3672 | 0.8336 | | 0.3977 | 3.0 | 867 | 0.4997 | 0.8276 | |... | c43a337051c22850aa247c0a0845955a |
mit | ['exbert'] | false | no-phone-gpt2 This is a test to remove memorized private information, such as phone numbers, from a small GPT-2 model. This should not generate valid phone numbers. Inspired by BAIR privacy research: - https://bair.berkeley.edu/blog/2019/08/13/memorization/ - https://bair.berkeley.edu/blog/2020/12/20/lmmem/ [Blog p... | 3e45769eff453a686790e8d376e8fd34 |
mit | ['exbert'] | false | tokens were replaced with new, randomly-selected 2- and 3-digit numbers in the vocab.json and tokenizer.json. You can identify these in outputs because the new tokens start with ^^. - Input and output embeddings for + | b1293373807f0b59306063ad126bacc7 |
mit | ['exbert'] | false | tokens were moved to the +00 and +000 embeddings. - Removed associations between numbers from merges.txt Using a library such as [ecco](https://github.com/jalammar/ecco), probabilities for next number token look equally likely, with +000 preferred. Code: https://colab.research.google.com/drive/1X31TIZjmxlXMXAzQrR3Fl... | fad3d52c083fda13fcf2311918aaddcf |
mit | ['exbert'] | false | BibTeX entry and citation info Original GPT-2: ```bibtex @article{radford2019language, title={Language Models are Unsupervised Multitask Learners}, author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, year={2019} } ``` | 39f4b090fef33ff61ae2a6a57365400b |
apache-2.0 | [] | false | This model is a BERT-based Location Mention Recognition model that is adopted from the [TLLMR4CM GitHub](https://github.com/rsuwaileh/TLLMR4CM/). The model identifies the toponyms' spans in the text and predicts their location types. The location type can be coarse-grained (e.g., country, city, etc.) and fine-grained ... | 6a00fec737923cd570834eaf40d12375 |
mit | ['generated_from_trainer'] | false | bart-cnn-science-v3-e2-v4-e2-manual This model is a fine-tuned version of [theojolliffe/bart-cnn-science-v3-e2](https://huggingface.co/theojolliffe/bart-cnn-science-v3-e2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9189 - Rouge1: 55.982 - Rouge2: 36.9147 - Rougel: 39.156... | 73f9cbea5538625dfa530c5bd7d4d539 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 42 | 0.9365 | 53.4332 | 34.0477 | 36.9735 | 51.1918 | 14... | 2d69ee1c18998b64f1c6186e419f4d87 |
apache-2.0 | ['translation'] | false | opus-mt-fi-kqn * source languages: fi * target languages: kqn * OPUS readme: [fi-kqn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-kqn/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | 323b70c89a4cb7379825ecfe06f2c580 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_stsb_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.2529 - Pearson: nan - Spearmanr: nan - Combined Score: nan | 9dbd6b38a188ad880ec67dacdfdb1eeb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 8.7243 | 1.0 | 23 | 6.6928 | nan | nan | nan | | 7.9215 | 2.0 | 46 ... | 0a3cf73e8ecbe2ccd8edbb897ce39ed1 |
apache-2.0 | ['generated_from_trainer'] | false | T5-model-1-d-6 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0229 - Rouge1: 94.972 - Rouge2: 84.9842 - Rougel: 94.7792 - Rougelsum: 94.758 - Gen Len: 15.0918 | 76af200089e9079f484bc8914273fa7d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:-------:|:---------:|:-------:| | 0.0449 | 1.0 | 16085 | 0.0229 | 94.972 | 84.9842 | 94.7792 | 94.758 | 15... | 32e76440bb274dd412845b184cdfda89 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 22700, 'end_learning_r... | 589c09a2f0572a22b7881a0b925bf21c |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr2e05-wd0.05-bs64 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2824 - Rmse: 0.5315 - Mse: 0.2824 - Mae: 0.4398 | 8c9ee6494a8ab22abb4d522d83dac3f7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2768 | 1.0 | 623 | 0.2742 | 0.5237 | 0.2742 | 0.4168 | | 0.2736 | 2.0 | 1246 | 0.2757 | 0.5251 | 0.2757 ... | 3de8915da22a7863767a4648bd7e1f48 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Hi - Sanchit Gandhi 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: 0.7019 - Wer: 60.0458 | 5e106abcab017adc4cc76387c41160de |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0048 | 20.0 | 1000 | 0.3577 | 60.3892 | | 0.0001 | 40.0 | 2000 | 0.4971 | 60.0458 | | 0.0001 | 60.0 | 3000 | 0.6355 | 60.675... | beb56ce1c28dc079c9e092cb51d67c1c |
apache-2.0 | ['translation'] | false | opus-mt-es-hr * source languages: es * target languages: hr * OPUS readme: [es-hr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-hr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | f6b26e971da2e9186654aa17a0396516 |
['cc0-1.0'] | ['reinforcement learning', 'cartpole', 'deep deterministic policy gradient'] | false | Keras Implementation of Deep Deterministic Policy Gradient ⏱🤖 This repo contains the model and the notebook [to this Keras example on Deep Deterministic Policy Gradient on pendulum](https://keras.io/examples/rl/ddpg_pendulum/). Full credits to: [Hemant Singh](https://github.com/amifunny)  is a model-free off-policy algorithm for learning continous actions. It combines ideas from DPG (Deterministic Policy Gradient) and DQN (Deep Q-Network). It uses Experience Replay and slow-learning target networks from DQN, and it is based on DPG, whic... | 708f2545a03bba3cc9295327c402c77c |
apache-2.0 | ['AnimeGanv3'] | false | Model Description Transforming photos of real-world scenes into anime style images is a meaningful and challenging task in terms of computer vision and artistic style transfer. AnimeGANv3_PortraitSketch Made by Asher Chan. The official code in [here](https://github.com/TachibanaYoshino/AnimeGANv2) | 6dcbaeafde881ac073a275015e9c55d4 |
apache-2.0 | ['AnimeGanv3'] | false | License This repo is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications. Permission is granted to use the AnimeGAN given that you agree to my license terms. Regarding the request for commercial use, please contact u... | 376c0847cc58ebcfaa53ae0178b9d3f5 |
cc-by-4.0 | ['answer extraction'] | false | Model Card of `lmqg/mbart-large-cc25-jaquad-ae` This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for answer extraction on the [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi41... | 8183b4e1da2d75ad37b110c01eb50cd9 |
cc-by-4.0 | ['answer extraction'] | false | Overview - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) - **Language:** ja - **Training data:** [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://g... | f6c29420790188bedc6c4c440ea424b7 |
cc-by-4.0 | ['answer extraction'] | false | model prediction answers = model.generate_a("フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め30数点しか現存しない。現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-jaquad-ae") output = pipe("... | 664d80e6c599e5c5862a36566f7aea04 |
cc-by-4.0 | ['answer extraction'] | false | Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-jaquad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_jaquad.default.json) | | Score | Type | Dataset ... | 3b0c97078384a0af2b0666f54b17a731 |
cc-by-4.0 | ['answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_jaquad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: None - model: facebook/mbart-large-cc25 - max_length: 512 - max_length_output: 32 - epoc... | 9cfbf5fe07bdcbb528d082d8dac9463a |
apache-2.0 | ['generated_from_keras_callback'] | false | TestZee/t5-base-finetuned-kaggle-data-t5-base This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.2080 - Validation Loss: 2.0110 - Train Rouge1: 24.3681 - Train Rouge2: 8.7734 - Train Rougel: 18... | be56ae33ffef8a7add51bd7cd272bca0 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.001} - training_precision: float32 | 4da7b35e894e49c58e98877cd7788123 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 2.2080 | 2.0110 | 24.3681 | 8.7734 ... | 7716d57ccd89c2837f35c4ceb699db36 |
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: 0.0586 - Precision: 0.9128 - Recall: 0.9406 - F1: 0.9265 - Accuracy: 0.9842 | bf947b9b64a21a88c52267f691d1b17c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 293 | 0.0844 | 0.8714 | 0.9123 | 0.8914 | 0.9760 | | 0.1765 | 2.0 |... | 503498a96dc3d533c64b3db0c70d0a7a |
mit | ['Transformers'] | false | MentalHealth-RoBERTa model: MentalHealth-RoBERTa is a roberta-large model (https://huggingface.co/roberta-large ) fine-tuned on a SMHD corpus. [SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions] (https://ir.cs.georgetown.edu/resources/smhd.html). We follow the st... | 954af386b1827b652fbf8405208d04f0 |
mit | ['Transformers'] | false | Usage Load the model via [Huggingface’s Transformers library](https://github.com/huggingface/transformers): from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Amalq/mental-health-roberta-large") model = AutoModel.from_pretrained("Amalq/mental-health-roberta-large")... | a21fb0bf940c8bc58f16135fe2dfb8de |
apache-2.0 | ['translation'] | false | opus-mt-fi-bzs * source languages: fi * target languages: bzs * OPUS readme: [fi-bzs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-bzs/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | e2c06802b11c2ed48288d99919a7c391 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__sst2__train-8-7 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6950 - Accuracy: 0.4618 | 882f14196d9b16100099f84ceacc8dc9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7156 | 1.0 | 3 | 0.6965 | 0.25 | | 0.6645 | 2.0 | 6 | 0.7059 | 0.25 | | 0.6368 | 3.0 | 9 | 0.7179 | 0.... | 1fb2090bd9601d725ec1843b671fe433 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0590 - Precision: 0.9270 - Recall: 0.9399 - F1: 0.9334 - Accuracy: 0.9844 | 2306711067ed98dbfecba61e51f73775 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2483 | 1.0 | 878 | 0.0696 | 0.9143 | 0.9211 | 0.9177 | 0.9807 | | 0.0504 | 2.0 |... | 70a62f68123cfc5a75a7d8bfa07626db |
apache-2.0 | [] | false | doc2query/msmarco-arabic-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-4... | aeb9e12e8e763b12081121c1009b2f4b |
apache-2.0 | [] | false | Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
model_name = 'doc2query/msmarco-arabic-mt5-base-v1'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
text = "بايثون (بالإنجليزية: Python) هي لغة برمجة، ع... | 9a7358c3d1b790972c990201f6603d27 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased.CEBaB_confounding.observational.absa.5-class.seed_42 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the OpenTable OPENTABLE-ABSA dataset. It achieves the following results on the evaluation set: - Loss: 0.5675 - Accuracy: 0.8662 - Macro-f1: 0.8... | d17d1b7db4cbba6777c20350cd3810d9 |
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: 0.8359 - Matthews Correlation: 0.5416 | bde365b9c3be7cd517590f0f55e7f491 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5239 | 1.0 | 535 | 0.5284 | 0.4297 | | 0.3437 | 2.0 | 1070 | 0.5006 | 0.5166 | | 0.2... | d109a80d5045d7fa21533839fb765486 |
apache-2.0 | [] | false | Model Card for Model ID This model is a finetuned version of [north/t5_small_NCC_modern](https://huggingface.co/north/t5_small_NCC_modern). | | Size |Model|BLEU| |:------------:|:------------:|:------------:|:------------:| |**Small** |**_60M_**|✔|**93.44**| |Base |_220M_|[🤗](https://huggingface.co/north/nynorsk... | 29a013452599096296760bfff601aee5 |
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