license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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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... | fa507aa5601fdbebbb6eb30a024da87e |
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... | ca0068b615e2fb354945897e98a8a94c |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_vp-fr_s600 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 80206c2e83121cb4b700540e72accecf |
cc-by-4.0 | ['generated_from_trainer'] | false | NLP-CIC-WFU_Clinical_Cases_NER_Sents_Tokenized_bertin_roberta_base_spanish_fine_tuned This model is a fine-tuned version of [bertin-project/bertin-roberta-base-spanish](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) on the None dataset. It achieves the following results on the evaluation set: - Lo... | 73ad7ef1ee853595e46a4c52d016a22d |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0605 | 1.0 | 2568 | 0.0625 | 0.9400 | 0.6322 | 0.7560 | 0.9836 | | 0.0475 | 2.0 ... | 1a44862ab3e8f0e9d0f788f2f2b2ee15 |
apache-2.0 | ['translation'] | false | pol-nor * source group: Polish * target group: Norwegian * OPUS readme: [pol-nor](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/pol-nor/README.md) * model: transformer-align * source language(s): pol * target language(s): nob * model: transformer-align * pre-processing: normalization + Sen... | 3a834567da8fcf8643c5adf95f8023b8 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: pol-nor - source_languages: pol - target_languages: nor - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/pol-nor/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['pl', 'no'] - src_constituents: {'pol'} - tgt_const... | 71fd593d3b503fe9ec5b62ff30f26152 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | Baseline Model trained on train5a1e8w7 to apply classification on label **Metrics of the best model:** accuracy 0.693101 recall_macro 0.665973 precision_macro 0.657625 f1_macro 0.656998 Name: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000), dtype: float64 **See m... | e1d472befad555f117f95c61795511b4 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;,EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless v_21 False False False ... False False False v_32 True False False ... False False False v_15 False False False ... False ... | 17c6d230ad39d2d13a12aa6c64c6ce04 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;,max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wra... | 0fed5bd41e5031e5dbd3d69a92ebcdd5 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;,max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">EasyP... | 77b158edeff183302ec9e7acbdc5c149 |
apache-2.0 | ['generated_from_trainer'] | false | daniel_asr 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: 0.4565 - Wer: 0.3423 | 20024e776d390c290b9d6e298c87df36 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4909 | 4.0 | 500 | 1.3485 | 0.8887 | | 0.5887 | 8.0 | 1000 | 0.4957 | 0.4641 | | 0.2207 | 12.0 | 1500 | 0.4621 | 0.3971 | |... | f99c76ffbfc6980cf089c7d226da0ea7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-hate-final This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6212 - Accuracy: 0.7253 - Precision: 0.7207 - Recall: 0.7253 - F1: 0.7206 ... | c85f545236acb9535f47094577d749ed |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.5760 | 0.7025 | 0.7053 | 0.7025 | 0.6771 | | 0.569 | 2.0 |... | 3104fc45702f7bd86423dd1c806477f5 |
mit | [] | false | Model description **bert-large-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). S... | a7a05237721d555e00ccf8f0807309fd |
mit | [] | false | How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER") model = AutoModelForTokenClassification.from_pretrained("dslim/... | 2a59f281a8637df5a256f9357ee209ba |
mit | [] | false | Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary t... | bf826850c6cb4aa2900abf97a8ca46d5 |
mit | [] | false | Training data This model was fine-tuned on English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset. The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type,... | 7906ab0d6d0f0df6451116cab13efbe4 |
mit | [] | false | CoNLL-2003 English Dataset Statistics This dataset was derived from the Reuters corpus which consists of Reuters news stories. You can read more about how this dataset was created in the CoNLL-2003 paper. | 7b4f305816714fc8ebfeefe99730ad1e |
mit | [] | false | Training procedure This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original BERT paper](https://arxiv.org/pdf/1810.04805) which trained & evaluated the model on CoNLL-2003 NER task. | afe8a0fa16138366b1eb6dbacf8a7f6a |
mit | [] | false | Eval results metric|dev|test -|-|- f1 |95.7 |91.7 precision |95.3 |91.2 recall |96.1 |92.3 The test metrics are a little lower than the official Google BERT results which encoded document context & experimented with CRF. More on replicating the original results [here](https://github.com/google-research/bert/issues/22... | 08160724324969c6fcfd1998012be072 |
mit | [] | false | BibTeX entry and citation info ``` @article{DBLP:journals/corr/abs-1810-04805, author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Unders... | 02d04b6c290e6162337d387e2fac2c6f |
apache-2.0 | ['korean'] | false | KoELECTRA v3 (Base Discriminator) Pretrained ELECTRA Language Model for Korean (`koelectra-base-v3-discriminator`) For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md). | 1d9e1bc82ebaee3ebc0cf4e04da94386 |
apache-2.0 | ['korean'] | false | Load model and tokenizer ```python >>> from transformers import ElectraModel, ElectraTokenizer >>> model = ElectraModel.from_pretrained("monologg/koelectra-base-v3-discriminator") >>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-v3-discriminator") ``` | 5cdd27841865f3e44af079385649a499 |
apache-2.0 | ['korean'] | false | Tokenizer example ```python >>> from transformers import ElectraTokenizer >>> tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-v3-discriminator") >>> tokenizer.tokenize("[CLS] 한국어 ELECTRA를 공유합니다. [SEP]") ['[CLS]', '한국어', 'EL', ' | b14d347038eccf8bc2e9b227c4a9148b |
apache-2.0 | ['korean'] | false | Example using ElectraForPreTraining ```python import torch from transformers import ElectraForPreTraining, ElectraTokenizer discriminator = ElectraForPreTraining.from_pretrained("monologg/koelectra-base-v3-discriminator") tokenizer = ElectraTokenizer.from_pretrained("monologg/koelectra-base-v3-discriminator") sente... | 048e9499cd2bb312ad28852245d40202 |
mit | ['multi-label'] | false | Description A Multi-label text classification model trained on a customer feedback data using DistilBert. Possible labels are: - Delivery (delivery status, time of arrival, etc.) - Return (return confirmation, return label requests, etc.) - Product (quality, complaint, etc.) - Monetary (pending transactions, refund, e... | 6eda8b9aeb6e5e52f1a2024baf665ae2 |
mit | ['multi-label'] | false | Usage ``` from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_mlc_v7_distil") model = AutoModelForSequenceClassification.from_pretrained("CouchCat/ma_mlc_v7_distil") ``` | 4326f9b27cac28d7fa71e98ed86fe7d5 |
mit | [] | false | Malika Favre Art Style on Stable Diffusion This is the `<malika-favre>` 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... | cb4bea73b821afb4710c62c2c26aef65 |
cc-by-4.0 | ['audio', 'automatic-speech-recognition', 'faroese', 'xlrs-53-faroese', 'ravnur-project', 'faroe-islands'] | false | wav2vec2-large-xlsr-53-faroese-100h The "wav2vec2-large-xlsr-53-faroese-100h" is an acoustic model suitable for Automatic Speech Recognition in Faroese. It is the result of fine-tuning the model "facebook/wav2vec2-large-xlsr-53" with 100 hours of Faroese data released by the Ravnur Project (https://maltokni.fo/en/) f... | 74db21a8bfa5b158d18688ffafe02ef9 |
cc-by-4.0 | ['audio', 'automatic-speech-recognition', 'faroese', 'xlrs-53-faroese', 'ravnur-project', 'faroe-islands'] | false | Load the processor and model. MODEL_NAME="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-faroese-100h" processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME) model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME) | cb5773fdb2ac5698327da9c422989c31 |
cc-by-4.0 | ['audio', 'automatic-speech-recognition', 'faroese', 'xlrs-53-faroese', 'ravnur-project', 'faroe-islands'] | false | Batched output is "un-batched" to ensure mapping is correct batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0] with processor.as_target_processor(): batch["labels"] = processor(batch["normalized_text"]).input_ids return batch ds = ds.map(prepare_d... | a802dd384c00b21da52d7ffe61443e71 |
cc-by-4.0 | ['audio', 'automatic-speech-recognition', 'faroese', 'xlrs-53-faroese', 'ravnur-project', 'faroe-islands'] | false | Define the evaluation metric import numpy as np wer_metric = load_metric("wer") def compute_metrics(pred): pred_logits = pred.predictions pred_ids = np.argmax(pred_logits, axis=-1) pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id pred_str = processor.batch_decode(pred_ids) | 317240f0e2c891a7e804cb3dda6d4df7 |
cc-by-4.0 | ['audio', 'automatic-speech-recognition', 'faroese', 'xlrs-53-faroese', 'ravnur-project', 'faroe-islands'] | false | We do not want to group tokens when computing the metrics label_str = processor.batch_decode(pred.label_ids, group_tokens=False) wer = wer_metric.compute(predictions=pred_str, references=label_str) return {"wer": wer} | 8c27f7692ba4a54202e9fb6835f661f8 |
cc-by-4.0 | ['audio', 'automatic-speech-recognition', 'faroese', 'xlrs-53-faroese', 'ravnur-project', 'faroe-islands'] | false | Do the evaluation (with batch_size=1) model = model.to(torch.device("cuda")) def map_to_result(batch): with torch.no_grad(): input_values = torch.tensor(batch["input_values"], device="cuda").unsqueeze(0) logits = model(input_values).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_... | e93faefeeeb819b2095fb5dcd76f753d |
cc-by-4.0 | ['audio', 'automatic-speech-recognition', 'faroese', 'xlrs-53-faroese', 'ravnur-project', 'faroe-islands'] | false | BibTeX entry and citation info *When publishing results based on these models please refer to:* ```bibtex @misc{mena2022xlrs53faroese, title={Acoustic Model in Faroese: wav2vec2-large-xlsr-53-faroese-100h.}, author={Hernandez Mena, Carlos Daniel}, year={2022}, url={https://huggingface.co/carlo... | 4a85838a8ac426a36da1160a4032eb70 |
cc-by-4.0 | ['audio', 'automatic-speech-recognition', 'faroese', 'xlrs-53-faroese', 'ravnur-project', 'faroe-islands'] | false | Acknowledgements We want to thank to Jón Guðnason, head of the Language and Voice Lab for providing computational power to make this model possible. We also want to thank to the "Language Technology Programme for Icelandic 2019-2023" which is managed and coordinated by Almannarómur, and it is funded by the Icelandic ... | 494db7485e0f2ca67e7f0d9d87e02add |
other | [] | false | Upholstery Cleaning Richardson TX https://carpetcleaning-richardson.com/upholstery-cleaning.html (972) 454-9815 Your furniture is the most expensive item in your home, along with probably your jewelry and electronics, cars, and other possessions.It's possible that some of this furniture was passed down through generati... | 2464f01af9f673b0ea7a0b967629556f |
mit | [] | false | Model Details **Model Description:** GPT-2 XL is the **1.5B parameter** version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective. - **Developed by:** OpenAI, see [associated research pape... | 0fee4cb162377a61a03d59361ed9a3dc |
mit | [] | false | How to Get Started with the Model Use the code below to get started with the model. 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... | 13a1fa74d6ac937956609a14223ef644 |
mit | [] | false | Direct Use In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote: > The primary intended users of these models are AI researchers and practitioners. > > We primarily imagine these language models will be used by researchers to better understand the behaviors, ca... | b1c2b046ba24a39ccb5782ba8c97943d |
mit | [] | false | Downstream Use In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote: > Here are some secondary use cases we believe are likely: > > - Writing assistance: Grammar assistance, autocompletion (for normal prose or code) > - Creative writing and art: exploring the g... | 2e0280bcaf5a42709bb022093d029b93 |
mit | [] | false | Misuse and Out-of-scope Use In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote: > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true. > > Additionally, lan... | 53ea68c90f4ba1d377192299c0ba4bdf |
mit | [] | false | Biases Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). The training data used for this model has not been released as a d... | e7f154fdb0b2c6d1cf33c99e46627ec5 |
mit | [] | false | Risks and Limitations When they released the 1.5B parameter model, OpenAI wrote in a [blog post](https://openai.com/blog/gpt-2-1-5b-release/): > GPT-2 can be fine-tuned for misuse. Our partners at the Middlebury Institute of International Studies’ Center on Terrorism, Extremism, and Counterterrorism (CTEC) found th... | dad5b353e626805908625978bc3a580e |
mit | [] | false | Training Data The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from this dataset, so the model was not trained on any part of Wikipedia. The... | 594f0b531bd354b784d066a24635b180 |
mit | [] | false | Training Procedure The model is pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels... | ea35715290341fce6c3940a71e567b76 |
mit | [] | false | Evaluation The following evaluation information is extracted from the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). | b8179e25955c22ec5facb7a3d13a653f |
mit | [] | false | Testing Data, Factors and Metrics The model authors write in the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) that: > Since our model operates on a byte level and does not require lossy pre-processing or tokenization, we can ev... | ac11da841b76add597fbca1686cfc854 |
mit | [] | false | Results The model achieves the following results without any fine-tuning (zero-shot): | Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW | |:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:| | (me... | 6fa593ba09da3795fef465469dd03cf3 |
mit | [] | false | compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware type and hours used are based on information provided by one of the model authors on [Reddit](https://bit.ly/2Tw1x4L). - **Hardware Type:** 32 TPUv3 chips - **Hours used:** 168 - **Cloud Provider:** Unknown - **Compute Region... | f55cb470852c99436407160aec48540a |
mit | [] | false | Technical Specifications See the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) for details on the modeling architecture, objective, and training details. | d82300ba99bc4e452bf603a4fd3d49fe |
mit | [] | false | Citation Information ```bibtex @article{radford2019language, title={Language models are unsupervised multitask learners}, author={Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya and others}, journal={OpenAI blog}, volume={1}, number={8}, pages={9}, ye... | 5d7a93c1a032e9b69b8c2dec1a30b23f |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab2 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: 0.7746 - Wer: 0.5855 | e9bcebc29b34219fa4b56ff4ad8cac24 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - 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: 800 - num_epochs: 35 - mixed_precision_tr... | 26644d23b6b88cfba0b328c34105e193 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.1452 | 13.89 | 500 | 2.9679 | 1.0 | | 1.075 | 27.78 | 1000 | 0.7746 | 0.5855 | | 0ad890ad9b31b87f2a2ddb4eb6097ad1 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-amrit-finetuned-amazon-en This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 3.3112 - Rouge1: 15.4603 - Rouge2: 7.1882 - Rougel: 15.2221 - Rougelsum: 15.1231 | 1157d5079d13caf9ed84383ce0565d6b |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 8.7422 | 1.0 | 771 | 3.6517 | 12.9002 | 4.8601 | 12.6743 | 12.6561 | | 4.1322 | 2.0 |... | 40a29b8a0df7e3ec98049a4e6983f448 |
apache-2.0 | ['generated_from_trainer'] | false | T5 (small) finetuned-turk-text-simplification This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1001 - Rouge2 Precision: 0.6825 - Rouge2 Recall: 0.4542 - Rouge2 Fmeasure: 0.5221 | 66293ea05f88c70005ac0e3d4e25bf2a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.4318 | 1.0 | 500 | 0.1053 | 0.682 | 0.4533 | 0.5214 ... | 011218dd1e522b05acebb04012b99120 |
mit | ['generated_from_trainer'] | false | bart-cnn-pubmed-arxiv-pubmed This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv) on the scientific_papers dataset. It achieves the following results on the evaluation set: - Loss: 1.9245 - Rouge1: 37.3328 - Rouge2: 15.5894 - Rougel: 23.... | 2aac7b0a97166ecafdc1984d3f3d9c8a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 2.0272 | 1.0 | 29981 | 1.9245 | 37.3328 | 15.5894 | 23.0297 | 33.952 ... | 70c6fb2617ac9b4218af67b1a2a81e30 |
cc-by-sa-4.0 | ['text-classification', 'hate-speech'] | false | bcms-bertic-frenk-hate Text classification model based on [`classla/bcms-bertic`](https://huggingface.co/classla/bcms-bertic) and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the Croatian subset of the data was used for fin... | 4225d3da54ad73ad9605fe414db9745c |
cc-by-sa-4.0 | ['text-classification', 'hate-speech'] | false | Fine-tuning hyperparameters Fine-tuning was performed with `simpletransformers`. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are: ```python model_args = { "num_train_epochs": 12, "learning_rate": 1e-5, "train_batch_size": 74} ``` | 11bce24b20256c3c9ace4b9421b8c8f5 |
cc-by-sa-4.0 | ['text-classification', 'hate-speech'] | false | Performance The same pipeline was run with two other transformer models and `fasttext` for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed. | model | average accuracy | average macro F1 | |----------------------------|--------... | d29f8b1d62d1b19ff327418d31f8fc87 |
cc-by-sa-4.0 | ['text-classification', 'hate-speech'] | false | Use examples ```python from simpletransformers.classification import ClassificationModel model = ClassificationModel( "bert", "5roop/bcms-bertic-frenk-hate", use_cuda=True, ) predictions, logit_output = model.predict(['Ne odbacujem da će RH primiti još migranata iz Afganistana, no neće biti novog vala', ... | b42e4801e276855f4d930ade41d380d1 |
cc-by-sa-4.0 | ['text-classification', 'hate-speech'] | false | Citation If you use the model, please cite the following paper on which the original model is based: ``` @inproceedings{ljubesic-lauc-2021-bertic, title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian", author = "Ljube{\v{s}}i{\'c}, Nikola and Lauc, Dav... | be2881f688991f08d215958645b0b63e |
apache-2.0 | ['generated_from_trainer'] | false | rubert-finetuned-collection3 This model is a fine-tuned version of [sberbank-ai/ruBert-base](https://huggingface.co/sberbank-ai/ruBert-base) on the collection3 dataset. It achieves the following results on the evaluation set: - Loss: 0.0514 - Precision: 0.9355 - Recall: 0.9577 - F1: 0.9465 - Accuracy: 0.9865 | 20540c5a4d44da1500b81b3d87faeb27 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0794 | 1.0 | 1163 | 0.0536 | 0.9178 | 0.9466 | 0.9320 | 0.9825 | | 0.0391 | 2.0 |... | bf7f875b62d67a7dd2db4856920d3734 |
apache-2.0 | ['generated_from_trainer'] | false | mt5-small-finetuned-ar-to-th-finetuned-ar-to-th-2nd-round-finetuned-ar-to-th-3rd-round This model is a fine-tuned version of [Shularp/mt5-small-finetuned-ar-to-th](https://huggingface.co/Shularp/mt5-small-finetuned-ar-to-th) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7393 ... | 0616ef0b3b35acb10391d46686987790 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5917 | 1.0 | 16806 | 2.8661 | 4.1430 | | 3.432 | 2.0 | 33612 | 2.7698 | 5.0779 | | 3.3793 | 3.0 | 50418 | 2.7393 | 5.386... | e69daffb47a8a64db325d57f3f9fc7c3 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-small_talk-4-16-5-oos This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3566 - Accuracy: 0.3855 | a7c692a92ff8e147081780616f53ab66 |
mit | [] | false | DistilCamemBERT-Sentiment ========================= We present DistilCamemBERT-Sentiment, which is [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) fine-tuned for the sentiment analysis task for the French language. This model is built using two datasets: [Amazon Reviews](https://huggingface.co/... | a41bd80f58d6e66f1526470e89628972 |
mit | [] | false | bert-base-multilingual-uncased-sentiment [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) is based on BERT model in the multilingual and uncased version. This sentiment analyzer is trained on Amazon reviews, similar to our model. Hence the targ... | c331086fe34581ad44663456b1759b23 |
mit | [] | false | tf-allociné and barthez-sentiment-classification [tblard/tf-allocine](https://huggingface.co/tblard/tf-allocine) based on [CamemBERT](https://huggingface.co/camembert-base) model and [moussaKam/barthez-sentiment-classification](https://huggingface.co/moussaKam/barthez-sentiment-classification) based on [BARThez](https... | debe7fae5f984c236d2ac1756b45017a |
mit | [] | false | Optimum + ONNX ```python from optimum.onnxruntime import ORTModelForSequenceClassification from transformers import AutoTokenizer, pipeline HUB_MODEL = "cmarkea/distilcamembert-base-sentiment" tokenizer = AutoTokenizer.from_pretrained(HUB_MODEL) model = ORTModelForSequenceClassification.from_pretrained(HUB_MODEL) o... | ebbd6a12445b704893c258cab2a56a21 |
mit | [] | false | reeducation camp on Stable Diffusion This is the `<reeducation-camp>` 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. ... | 9a36c33e886f02a1bc86142776d2ae18 |
apache-2.0 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_no-pretraining_s20 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been... | 5409a2d474fd43ecf4c807ddf6bf49bb |
apache-2.0 | ['generated_from_trainer', 'es', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-spanish-large This model is a fine-tuned version of [tomascufaro/xls-r-es-test](https://huggingface.co/tomascufaro/xls-r-es-test) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.1431 - Wer: 0.1197 | 3ff7c70f3914890d5cbfdc473c0aa0d3 |
apache-2.0 | ['generated_from_trainer', 'es', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 10 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 20 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | b07b011d432b65349f006496972b2308 |
apache-2.0 | ['generated_from_trainer', 'es', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.1769 | 0.15 | 400 | 0.1795 | 0.1698 | | 0.217 | 0.3 | 800 | 0.2000 | 0.1945 | | 0.2372 | 0.45 | 1200 | 0.1985 | 0.185... | 2035060dbc2d27860d5d8efd2a3fb1cc |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-sentiment-finetuned-memes 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: 1.1824 - Accuracy: 0.8270 - Precision: 0.8270 - Recall: 0.8270 - F1: 0... | 2f4bb4547897840f9b0f9c9fb5dbced9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 | 59847bc8fedfc006e39fb70844208ea2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.5224 | 1.0 | 4293 | 0.5321 | 0.7720 | 0.8084 | 0.7720 | 0.7721 | | 0.4386 | 2.0 ... | 4666c19527976a594870468a746593c6 |
apache-2.0 | ['generated_from_keras_callback'] | false | Krishadow/biobert-finetuned-ner-K 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: 0.0099 - Validation Loss: 0.0676 - Epoch: 4 | ac48193b43ae22396c6442a91ca4518a |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1222 | 0.0604 | 0 | | 0.0398 | 0.0531 | 1 | | 0.0220 | 0.0616 | 2 | | 0.0134 | 0.0653 | 3 | | 0.0099 | 0.0676 | 4 | | 6086285692ceac987dc90ec72ff1974f |
apache-2.0 | ['generated_from_keras_callback'] | false | my-awesome-model 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: 0.8153 - Validation Loss: 0.4165 - Epoch: 0 | 0ecf852cf4e9a943002bb6543545a154 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Prompt example : (anthropomorphic) chicken rckrll, closeup Negative : painting, fake, drawing 768x768 Each 5 steps can make a big output difference with the same seed. You can use the images below to load the full settings in A1111 A1111 Colab :[fast-stable-diffusion-A1111](https://colab.research.google.com/git... | 8977f35471f9254254aa1459b4f59667 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Sample pictures of this concept: .png) .png)  on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1914 - Wer: 1.0 | f816ec23be4b9a9ed4461c67309da975 |
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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 700 - num_epochs: 30 - mixed_precision_trai... | 94edfdabfe633028cb77620b388126d3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 3.8196 | 7.04 | 500 | 3.2201 | 1.0 | | 3.1517 | 14.08 | 1000 | 3.1876 | 1.0 | | 3.1493 | 21.13 | 1500 | 3.1837 | 1.0 | | 3.1438 ... | be27ae61847ad06d295e4f912dfc5747 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | glpn-nyu-finetuned-diode-221221-102136 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.4222 - Mae: 0.4110 - Rmse: 0.6292 - Abs Rel: 0.3778 - Log Mae: 0.1636 - Log Rmse:... | f57d73ad7fc34b85e7cfef05b8ef2800 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 24 - eval_batch_size: 48 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.15 - num_epochs: 10 - mixed_precisio... | d9d564ce97ccd215f6bc379fc43b6f76 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| | 0.4953 | 1.0 | 72 | 0.4281 ... | 258d5a79aa30640d356a27cc7fb2dc38 |
apache-2.0 | ['generated_from_trainer'] | false | resnet-50-finetuned-eurosat This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.9095 - Accuracy: 0.8240 | 38fcef105bba063db96090503c1478de |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | a2258e193e8e44160dfe76a2695c819a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.78 | 0.96 | 17 | 1.7432 | 0.4321 | | 1.7105 | 1.96 | 34 | 1.6596 | 0.6307 | | 1.6045 | 2.96 | 51 | 1.5369 | 0.... | cbf83338c8e25d5db5c7670a4d8abe9f |
mit | [] | false | Nathan-Wyatt on Stable Diffusion This is the `<Nathan-Wyatt>` 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 ... | eb091d268f20f116aba7a28078d3c088 |
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