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', 'id'] | false | exp_w2v2t_id_vp-es_s425 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 1eef05bffda5f944f7c02f4cc3e6d092 |
apache-2.0 | ['translation'] | false | fra-tgl * source group: French * target group: Tagalog * OPUS readme: [fra-tgl](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-tgl/README.md) * model: transformer-align * source language(s): fra * target language(s): tgl_Latn * model: transformer-align * pre-processing: normalization + ... | 3816dd81f043301ebc8d82bf331bd7d3 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: fra-tgl - source_languages: fra - target_languages: tgl - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-tgl/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['fr', 'tl'] - src_constituents: {'fra'} - tgt_const... | aa38f0a26b6d171094d369e51667a75c |
apache-2.0 | ['translation'] | false | opus-mt-el-fi * source languages: el * target languages: fi * OPUS readme: [el-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/el-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 153da639bb02ce18cd906e8dd5338a10 |
apache-2.0 | ['generated_from_trainer'] | false | finetune_hate_speech_improved_v1 This model is a fine-tuned version of [cross-encoder/ms-marco-electra-base](https://huggingface.co/cross-encoder/ms-marco-electra-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5548 - Accuracy: 0.8277 - F1: 0.8416 - Precision: 0.7883 - Re... | c757a3b6b58ec8ff9d9aa335e669c794 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Informations Fine-tuned SD v2-1 model, 10400 steps, 5 epochs Aspect Ratio Bucketing centered at 768 resolution, aspect ratio 16:9 (1024x576) Made with 208 pictures of the movie Redline by MadHouse; Captions by WD-v1-4 | a7494db6e89a9e9cf4101651cdb5efde |
apache-2.0 | ['generated_from_keras_callback'] | false | tmpmrwiph1p 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: 0.1382 - Train Accuracy: 0.9482 - Validation Loss: 0.7241 - Validation Accuracy: 0.8109 - Epoch: 1 | 1ff404323fc836eed9fb647a8df6d250 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.3773 | 0.8313 | 0.4627 | 0.8131 | 0 | | 0.1382 | 0.9482 | 0.7241 | 0.8109 ... | e15c35da1e8f04c56dfd077beb4c5cbb |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_EN This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on the CRAFT dataset. It achieves the following results on the evaluation set: - Loss... | ab39e15d7b63216c2fc76f1b58c46097 |
apache-2.0 | ['generated_from_trainer'] | false | Model description This model performs Named Entity Recognition for 6 entity tags: Sequence, Cell, Protein, Gene, Taxon, and Chemical from the CRAFT(Colorado Richly Annotated Full Text) Corpus in English. Entity tags have been normalized and replaced from the original three letter code to a full name e.g. B-Protein, I... | e7160efee2e0fb83bf0cb249284d045c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0842 | 1.0 | 2719 | 0.1765 | 0.7606 | 0.7785 | 0.7695 | 0.9542 | | 0.0392 | 2.0 ... | d2142b18dc0dbbde8b30bd2b0f384f15 |
apache-2.0 | ['generated_from_trainer'] | false | bert-tiny-sst2-KD-distilBERT This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.1035 - Accuracy: 0.8326 | b3b423837a2a064e4e681f8d8ec82343 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.2008 | 1.0 | 4210 | 1.1319 | 0.8177 | | 0.6821 | 2.0 | 8420 | 1.1035 | 0.8326 | | 0.5315 | 3.0 | 12630 | 1.2271 ... | 29e72be0c18a21b1f569ef15f54b5252 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/bert-large-nli-cls-token This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 82990b545ee3844211ff584003e97307 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | 21e4f478bb511984e58e41ea18d8ce4c |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/bert-large-nli-cls-token') model = AutoModel.from_pretrained('sentence-transformers/bert-large-nli-cls-token') | 22ca8fab71395a56d04d5b8096a6cfc9 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/bert-large-nli-cls-token) | 88f9762084b6a4db6ad1654dba8d9fc3 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_me... | 36a3f41ffd9a4adc6f3b1b3070b286b4 |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_EN This model is a fine-tuned version of [StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_EN](https://huggingface.co/StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_EN) on the ... | fde1440ea9aac4c7e614496bdaa8736a |
apache-2.0 | ['generated_from_trainer'] | false | Model description This model performs Named Entity Recognition for 6 entity tags: Sequence, Cell, Protein, Gene, Taxon, and Chemical from the CRAFT(Colorado Richly Annotated Full Text) Corpus in Spanish and English. Entity tags have been normalized and replaced from the original three letter code to a full name e.g. ... | 8632f33cb653256ca10c35a7f6bb91bc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0129 | 1.0 | 1360 | 0.2119 | 0.8404 | 0.8364 | 0.8384 | 0.9666 | | 0.0072 | 2.0 |... | f6e237710bb7f9dc3a656fe246dae72f |
apache-2.0 | ['generated_from_trainer'] | false | Religion-Classification 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: - Loss: 0.0585 - Accuracy: 0.9926 | f81983f00b98601c8806f4e2a67453f8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.0722 | 1.0 | 6947 | 0.0671 | 0.9855 | | 0.0368 | 2.0 | 13894 | 0.0470 | 0.9907 | | 0.0205 | 3.0 | 20841 | 0.0431 ... | 031ae228b5836345ca3295fff0d65b7c |
apache-2.0 | ['generated_from_keras_callback'] | false | Tf-base 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: | bf2e102186e00123dc7621412a4529d0 |
creativeml-openrail-m | ['text-to-image'] | false | Timmmy Dreambooth model trained by layvizu with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob... | 2a633f2f96ff65a5b4fe944b2b4d8f93 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_vp-100k_gender_male-5_female-5_s186 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... | 377ad1050e024b01542e4844162f6abd |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-rw Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Kinyarwanda using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset, using about 25% of the training data (limited to utterances without downvotes and shorter with 9... | a5f59a0f02a94d194d55de8de58d7cdb |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | a642948e25b43b4b91d721b2653fd58b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | WARNING! This will download and extract to use about 80GB on disk. test_dataset = load_dataset("common_voice", "rw", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("lucio/wav2vec2-large-xlsr-kinyarwanda") model = Wav2Vec2ForCTC.from_pretrained("lucio/wav2vec2-large-xlsr-kinyarwanda") resampler = ... | cd2e67358da3136702ce2b74d46f9844 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset[:2]["... | fb819193acba8966e5c6921502391364 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Kinyarwanda test data of Common Voice. Note that to even load the test data, the whole 40GB Kinyarwanda dataset will be downloaded and extracted into another 40GB directory, so you will need that space available on disk (e.g. not possible in the free tier of Goo... | a51dd49901d063aa3a16618dabc76bdb |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Audio pre-processing resampler = torchaudio.transforms.Resample(48_000, 16_000) def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() batch["sampling_rate"] = 16_000 return batch def cv_prepare(batc... | a3481c9c082e1421ac99c40cf1ef2307 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the audio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch... | ae58676ddf90bed6cf9ed0389db931d3 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training Examples from the Common Voice training dataset were used for training, after filtering out utterances that had any `down_vote` or were longer than 9.5 seconds. The data used totals about 125k examples, 25% of the available data, trained on 1 V100 GPU provided by OVHcloud, for a total of about 60 hours: 20 e... | a7c624643ae59e82e80a22e2b0506c8b |
mit | [] | false | [Korean BART](https://huggingface.co/hyunwoongko/kobart) model for paraphrasing. The dataset utilized can be found on the *Files and versions* tab under the name dataset.csv. ```python import torch from transformers import BartForConditionalGeneration, AutoTokenizer device = torch.device("cuda" if torch.cuda.is_ava... | 1749b6d14364e8ac55856c8be6945cec |
apache-2.0 | [] | false | The **AraRoBERTa** models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper [click](https://aclanthology.org/2022.wanlp-1.24/). The following are the AraRoBERTa seven dialectal variations: * AraRoBERTa-SA: Saudi Arabia (SA... | eb2b7bcdd3a4ab63183f3a819f7a309b |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0011 | 427b824a721517e5ae073bff8929f142 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 5 | 2.1485 | | No log | 2.0 | 10 | 2.0983 | | No log | 3.0 | 15 | 2.0499 | | No log | 4.0 | 20 | 2.0155 ... | be5c48d332d8848bdbdc6c566edd814d |
apache-2.0 | [] | false | Mengzi-BERT base model (Chinese) Pretrained model on 300G Chinese corpus. Masked language modeling(MLM), part-of-speech(POS) tagging and sentence order prediction(SOP) are used as training task. [Mengzi: A lightweight yet Powerful Chinese Pre-trained Language Model](https://arxiv.org/abs/2110.06696) | 5cf313e0d1c50fa66b6b35953b92fc34 |
apache-2.0 | [] | false | Usage ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained("Langboat/mengzi-bert-base") model = BertModel.from_pretrained("Langboat/mengzi-bert-base") ``` | 4d49e1a0534fe2448b7649ae5e2e2be1 |
apache-2.0 | [] | false | Scores on nine chinese tasks (without any data augmentation) | Model | AFQMC | TNEWS | IFLYTEK | CMNLI | WSC | CSL | CMRC2018 | C3 | CHID | |-|-|-|-|-|-|-|-|-|-| |RoBERTa-wwm-ext| 74.30 | 57.51 | 60.80 | 80.70 | 67.20 | 80.67 | 77.59 | 67.06 | 83.78 | |Mengzi-BERT-base| 74.58 | 57.97 | 60.68 | 82.12 | 87.50 | 85.40 | ... | 9c2ae7e79894f003614f2581e32009f6 |
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: 0.0675 - Precision: 0.9342 - Recall: 0.9424 - F1: 0.9383 - Accuracy: 0.9847 | dedd392849cf110a1eb0c6e5564408e0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.236 | 1.0 | 878 | 0.0705 | 0.9153 | 0.9239 | 0.9196 | 0.9812 | | 0.0493 | 2.0 |... | c3e5347e30cd10ffe0f054e9e51fdd40 |
apache-2.0 | ['generated_from_keras_callback'] | false | emotionClassifier 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: 0.3738 - Validation Loss: 0.9834 - Epoch: 4 | 46cafd70431bcd508bf27fcf4c48baff |
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': 18565, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'bet... | cea50487ee259c316fe10d5dcebb7efd |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.9929 | 0.8342 | 0 | | 0.7345 | 0.8298 | 1 | | 0.5943 | 0.8536 | 2 | | 0.4666 | 0.9231 | 3 | | 0.3738 | 0.9834 | 4 | | 5e9caf832191dc968dfe28fc5537e936 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/domain_transfer_clinic_credit_cards-massive_transport-roberta-large-v1-2-5 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transfor... | c2cd59095ee9e7afe3962a2ae99d12d0 |
apache-2.0 | ['generated_from_keras_callback'] | false | TEdetection_distiBERT_NER_final_8e This model is a fine-tuned version of [FritzOS/TEdetection_distiBERT_mLM_final_8e](https://huggingface.co/FritzOS/TEdetection_distiBERT_mLM_final_8e) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0032 - Validation Loss: 0.0037 - Epoc... | 91655c24690a4b213500fd9fe3fa0330 |
apache-2.0 | ['masked-lm'] | false | SR-RoBERTa base model Pretrained model on Serbian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between скопје and Скопје. | b5c5dd4898170a0f18059c8005c7d782 |
apache-2.0 | ['masked-lm'] | false | How to use You can use this model directly with a pipeline for masked language modeling: \ from transformers import pipeline \ unmasker = pipeline('fill-mask', model='macedonizer/sr-roberta-base') \ unmasker("Београд је <mask> град Србије.") \ [{'score': 0.7834128141403198, 'sequence': 'Београд је главни град Срби... | e07822be158a4a5c59f885c0e6020c6c |
apache-2.0 | ['generated_from_trainer'] | false | bert-fine-tuned-cola This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8408 - Matthews Correlation: 0.5981 | 68bd6bfe63b4b8084fd4c684d77a0c5c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4729 | 1.0 | 1069 | 0.5311 | 0.5154 | | 0.3134 | 2.0 | 2138 | 0.6336 | 0.6007 | | 0.1... | 1b4360d47c3641c904a2fa9ecd2fd16b |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-esg-ner This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.3380 - Precision: 0.5073 - Recall: 0.4847 - F1: 0.4957 - Accuracy: 0.8927 | 5b879a2619f9e63d6e2649110d2fb2e4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.4859 | 1.0 | 1756 | 0.3975 | 0.4732 | 0.4137 | 0.4415 | 0.8766 | | 0.331 | 2.0 |... | 7aab05c68805e84576c207ba2c07f8d1 |
apache-2.0 | ['generated_from_trainer'] | false | albert-base-v2-finetuned-squad This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.9650 | 46636137310f2a799ea5fc36a7398982 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.0595 | 1.0 | 8248 | 1.4663 | | 0.6228 | 2.0 | 16496 | 0.8433 | | 0.4347 | 3.0 | 24744 | 0.9650 | | 279ba4d5b73936a26ae8a161e87f7910 |
mit | [] | false | RandomPrompt-v1 A fine tuned GPT-neo 125M The purpose of this model is to autocomplete or generate danbooru-like prompts for generating images in Stable Diffusion derivatives that use danbooru tags for text conditioning. | d0096a726f72d87b296fb3fadf22020d |
mit | [] | false | Training Trained on 400k tags from danbooru posts for 600k steps, or around 0.25 epochs https://wandb.ai/saltacc/RandomPrompt/runs/2v2arf0u?workspace=user-saltacc I plan on doing further runs on better hardware to try to get more accurate prompt completion | e70fbce95145bcc24df5c02f71880b58 |
mit | [] | false | Model trained on IBMArgRank30k for 2 epochs with a learning rate of 3e-5 (optimised via grid search) in a similar way as in Lauscher et al. 2020 (see below). The original model was Tensorflow-based. This model corresponds to a reimplementation with Transformers & PyTorch. ``` @inproceedings{lauscher-etal-2020-rhetoric... | 29816294cba718a5eb5bd6805a3c4801 |
mit | ['generated_from_trainer'] | false | deberta-base-finetuned-squad-pruned0.1 This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 2.3741 | 7ce73725df018cbda26c5d1ed39548dd |
apache-2.0 | ['automatic-speech-recognition', 'th'] | false | exp_w2v2t_th_unispeech-ml_s256 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When us... | f0f3a92b0fa1d10139a98a2851951cd0 |
apache-2.0 | ['spacy', 'token-classification'] | false | DaCy medium transformer DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analysing Danish pipelines. DaCy's largest pipeline has achieved State-of-the-Art performance on Named entity recognition, part-of-speech tagging and dependency parsing for Danish on th... | c5f5b48031d96d02812a71eb818515c6 |
apache-2.0 | ['spacy', 'token-classification'] | false | danish-dependency-treebank-dane) (Rasmus Hvingelby, Amalie B. Pauli, Maria Barrett, Christina Rosted, Lasse M. Lidegaard, Anders Søgaard)<br />[Maltehb/danish-bert-botxo](https://huggingface.co/Maltehb/danish-bert-botxo) (BotXO.ai) | | **License** | `Apache-2.0 License` | | **Author** | [Centre for Humanities Computing... | c8afd3843223b09503d8ffc26cc70e11 |
apache-2.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `POS_ACC` | 97.44 | | `MORPH_ACC` | 97.24 | | `DEP_UAS` | 87.15 | | `DEP_LAS` | 83.97 | | `SENTS_P` | 87.30 | | `SENTS_R` | 87.77 | | `SENTS_F` | 87.53 | | `LEMMA_ACC` | 84.91 | | `ENTS_F` | 81.79 | | `ENTS_P` | 81.70 | | `ENTS_R` | 81.88 | | `TRANSFORMER_LOSS` | 1224302.39 |... | e89a4ebe3e7c3973b5e59fe87d908179 |
apache-2.0 | ['spacy', 'token-classification'] | false | Deterministic Augmentations Deterministic augmentations are augmentation which always yield the same result. | Augmentation | Part-of-speech tagging (Accuracy) | Morphological tagging (Accuracy) | Dependency Parsing (UAS) | Dependency Parsing (LAS) | Sentence segmentation (F1) | Lemmatization (Accuracy) | Named entit... | a2af17b54c829c0cfc14fe1526942f45 |
apache-2.0 | ['spacy', 'token-classification'] | false | Stochastic Augmentations Stochastic augmentations are augmentation which are repeated mulitple times to estimate the effect of the augmentation. | Augmentation | Part-of-speech tagging (Accuracy) | Morphological tagging (Accuracy) | Dependency Parsing (UAS) | Dependency Parsing (LAS) | Sentence segmentation (F1) | Le... | da1d4ca3b92a1fca45a0e562afad7cf2 |
apache-2.0 | ['generated_from_trainer'] | false | STT_Model_5 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: 0.1410 - Wer: 0.1808 | f2d7f57622be1097302df6f4da21f405 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 80 | 3778e56301d39d9a0fe963ff2407bda2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.882 | 5.68 | 500 | 0.5476 | 0.9998 | | 0.4219 | 11.36 | 1000 | 0.2672 | 0.7620 | | 0.1972 | 17.05 | 1500 | 0.1670 | 0.4849 | |... | 18f9436d88613d042bdaf9f9ad51940b |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the cpg-products concept trained by llhbr on the llhbr/dreamboot dataset. This is a Stable Diffusion model fine-tuned on the cpg-products concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of cpg-products beer** This model was created as part of the DreamBooth ... | b336a88c5bae33b2cb0fd6933d77bf73 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_cola_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6759 - Matthews Correlation: 0.0930 | 0b8db17f44a81a76bfa040b5c837c2e8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:-----:|:---------------:|:--------------------:| | 0.6287 | 1.0 | 1669 | 0.6759 | 0.0930 | | 0.5492 | 2.0 | 3338 | 0.7164 | 0.0719 | |... | fb9bd050ecf46ea0197b3b78c58ba6ae |
creativeml-openrail-m | ['text-to-image'] | false | t123rail Dreambooth model trained by duja1 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob... | 896b1b9dbc3b4ca7e268a8681c680af0 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_140k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 2, Step 140k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different ... | 66daed81dda57a5c35c9015400a63707 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_140k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_140k') model = TFBertModel.from_pretrained("google/multibe... | c5e1073e0e8887c5454246e4d1a8eda3 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-infovqa 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: 2.8276 | 5a6c5e27c08010377b605e5459ffbeb9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 250500 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 72d235178edd94cbedd6203856fc6c4e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.2765 | 0.23 | 1000 | 3.0678 | | 2.9987 | 0.46 | 2000 | 2.9525 | | 2.826 | 0.69 | 3000 | 2.7870 | | 2.7084 | 0.93 | 4000 | 2.7051 ... | 5ec882e82e76d2c9a5a321a4a574ef52 |
mit | ['generated_from_trainer'] | false | bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8065 - Rouge1: 54.5916 - Ro... | 8c1f256be8076dbda7a44f20111b3bbe |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 1.2945 | 1.0 | 795 | 0.9555 | 51.91 | 32.0926 | 33.6727 | 49.5306 | ... | 764110324e7aeae0b40d8a501ed8bd5a |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | MultiBERTs Seed 1 Checkpoint 1600k (uncased) Seed 1 intermediate checkpoint 1600k 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... | 045d60c141df8dd5910a072eb50427a2 |
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-1-1600k') model = BertModel.from_pretrained("multiberts-seed-1-1600k") text = "Replace me by any text you'd lik... | f9f3c3b3d7a5bc1e77a5aef0079217ff |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-demo-F03-2 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4472 - Wer: 0.8797 | 590e63a28354bd6d7135f38362966ae9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 24.7815 | 0.97 | 500 | 3.3881 | 1.0 | | 3.3791 | 1.94 | 1000 | 3.2550 | 1.0 | | 2.9748 | 2.91 | 1500 | 2.8719 | 1.0 ... | fe9fb5a944dc9996a641d301716e7cb9 |
apache-2.0 | ['generated_from_keras_callback'] | false | nlptest This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.9241 - Validation Loss: 2.5831 - Train Rougel: tf.Tensor(0.19511123, shape=(), dtype=float32) - Epoch: 0 | 595f71497be0ddc2344ce65970d0ba10 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False... | e19631b6c417eabf7292b74d7f6b4f6e |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rougel | Epoch | |:----------:|:---------------:|:----------------------------------------------:|:-----:| | 2.9241 | 2.5831 | tf.Tensor(0.19511123, shape=(), dtype=float32) | 0 | | 440d3bea6a7a48d731c2860f7a28b775 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned-marktextepoch-n200 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0880 | e57aa8f7fe2d0a1a108c4a4098566f8f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 200 | feaac2637022eb3edf0e0b881d67c670 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 2.5742 | 1.0 | 1606 | 2.4071 | | 2.4441 | 2.0 | 3212 | 2.2715 | | 2.3699 | 3.0 | 4818 | 2.2896 | | 2.3074 | 4.0 | 6424 | 2... | 0c5e19bb9031915473443b64a3e9498a |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xlsr-53-espeak-cv-ft-sah2-ntsema-colab This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3586 - Wer: 0.3296 | 3aa1f2c2b642a4fa6f44642e50ae2c10 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4128 | 5.71 | 400 | 0.4462 | 0.5733 | | 0.2344 | 11.43 | 800 | 0.3489 | 0.3969 | | 0.1181 | 17.14 | 1200 | 0.3470 | 0.3602 | |... | e3155ec952cfe6b03c9266f5de40e8fc |
apache-2.0 | ['generated_from_keras_callback'] | false | reecejocumsenbb/testfield-finetuned-imdb 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: 4.0451 - Validation Loss: 3.9664 - Epoch: 0 | 782de40d46c7b3f36dd30f17ea6dbbf5 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps... | 2c3a261ebd7b32273d098ed01d3505e8 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Monet-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-dif... | bafffa6dcb11b04738aaf34b9782d3ab |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-German Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on German using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. | d4d7abd6f5e8d1b422a8c03ed5cc49fb |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "de", split="test[:8]") | 661daea3d2395a28bba287e274853bde |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | use a batch of 8 for demo purposes processor = Wav2Vec2Processor.from_pretrained("maxidl/wav2vec2-large-xlsr-german") model = Wav2Vec2ForCTC.from_pretrained("maxidl/wav2vec2-large-xlsr-german") resampler = torchaudio.transforms.Resample(48_000, 16_000) """ Preprocessing the dataset by: - loading audio files - resa... | 57f95b832ebe3f2150506e0fd56697ce |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | run forward with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"]) """ Example Result: Prediction: [ 'zieh du... | 25a6da720c3a02577ea09ccad79e0442 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the German test data of Common Voice: ```python import re import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor """ Evaluation on the full test set: - takes ~20mins (RTX 3090). - r... | 94d85e245f689b9fa5020cc550d6e2c5 |
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