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 | ['generated_from_trainer'] | false | Sentiment140_ELECTRA_5E This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the sentiment140 dataset. It achieves the following results on the evaluation set: - Loss: 0.5410 - Accuracy: 0.84 | da37d9c2e37642386197d6ed6431efff |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6896 | 0.08 | 50 | 0.6605 | 0.7133 | | 0.6664 | 0.16 | 100 | 0.6054 | 0.7133 | | 0.5915 | 0.24 | 150 | 0.4777 | 0.8333 | | 0.5053 | 0.32 | 200 | 0.4735 | 0.7733 | | 0.4946 | 0.4 | 250 | 0.3847 | 0.8267 | | 0.4578 | 0.48 | 300 | 0.4025 | 0.8067 | | 0.4724 | 0.56 | 350 | 0.3642 | 0.8333 | | 0.4309 | 0.64 | 400 | 0.3762 | 0.86 | | 0.4818 | 0.72 | 450 | 0.3829 | 0.84 | | 0.416 | 0.8 | 500 | 0.3599 | 0.8467 | | 0.4201 | 0.88 | 550 | 0.3469 | 0.8533 | | 0.3664 | 0.96 | 600 | 0.3462 | 0.8467 | | 0.4289 | 1.04 | 650 | 0.3470 | 0.86 | | 0.3859 | 1.12 | 700 | 0.3440 | 0.8533 | | 0.3599 | 1.2 | 750 | 0.3475 | 0.8533 | | 0.3287 | 1.28 | 800 | 0.3524 | 0.8467 | | 0.3331 | 1.36 | 850 | 0.3475 | 0.8733 | | 0.3236 | 1.44 | 900 | 0.3657 | 0.8467 | | 0.3502 | 1.52 | 950 | 0.3525 | 0.84 | | 0.3702 | 1.6 | 1000 | 0.3655 | 0.8333 | | 0.3323 | 1.68 | 1050 | 0.3405 | 0.84 | | 0.3452 | 1.76 | 1100 | 0.3376 | 0.8533 | | 0.3742 | 1.84 | 1150 | 0.3481 | 0.8533 | | 0.3145 | 1.92 | 1200 | 0.3472 | 0.86 | | 0.3657 | 2.0 | 1250 | 0.3302 | 0.8733 | | 0.2601 | 2.08 | 1300 | 0.3612 | 0.86 | | 0.2954 | 2.16 | 1350 | 0.3640 | 0.8533 | | 0.2888 | 2.24 | 1400 | 0.3670 | 0.8467 | | 0.2572 | 2.32 | 1450 | 0.4118 | 0.84 | | 0.2955 | 2.4 | 1500 | 0.3811 | 0.86 | | 0.2431 | 2.48 | 1550 | 0.4221 | 0.84 | | 0.318 | 2.56 | 1600 | 0.3844 | 0.8467 | | 0.2615 | 2.64 | 1650 | 0.4109 | 0.8333 | | 0.2389 | 2.72 | 1700 | 0.4420 | 0.8467 | | 0.2983 | 2.8 | 1750 | 0.4203 | 0.8467 | | 0.2828 | 2.88 | 1800 | 0.3629 | 0.8733 | | 0.2897 | 2.96 | 1850 | 0.3916 | 0.8733 | | 0.2239 | 3.04 | 1900 | 0.4143 | 0.86 | | 0.2093 | 3.12 | 1950 | 0.4521 | 0.84 | | 0.2438 | 3.2 | 2000 | 0.4271 | 0.8467 | | 0.2282 | 3.28 | 2050 | 0.4548 | 0.8333 | | 0.1918 | 3.36 | 2100 | 0.4533 | 0.86 | | 0.1698 | 3.44 | 2150 | 0.5177 | 0.84 | | 0.2765 | 3.52 | 2200 | 0.4884 | 0.84 | | 0.2282 | 3.6 | 2250 | 0.4697 | 0.8533 | | 0.239 | 3.68 | 2300 | 0.4766 | 0.8533 | | 0.2219 | 3.76 | 2350 | 0.4628 | 0.8533 | | 0.2375 | 3.84 | 2400 | 0.4704 | 0.8533 | | 0.1883 | 3.92 | 2450 | 0.4744 | 0.84 | | 0.2049 | 4.0 | 2500 | 0.4977 | 0.84 | | 0.1958 | 4.08 | 2550 | 0.4906 | 0.84 | | 0.1656 | 4.16 | 2600 | 0.5219 | 0.8333 | | 0.1543 | 4.24 | 2650 | 0.5379 | 0.8333 | | 0.2082 | 4.32 | 2700 | 0.5107 | 0.84 | | 0.1724 | 4.4 | 2750 | 0.5208 | 0.84 | | 0.1778 | 4.48 | 2800 | 0.5238 | 0.84 | | 0.1914 | 4.56 | 2850 | 0.5325 | 0.84 | | 0.2436 | 4.64 | 2900 | 0.5279 | 0.84 | | 0.1662 | 4.72 | 2950 | 0.5295 | 0.84 | | 0.1288 | 4.8 | 3000 | 0.5392 | 0.84 | | 0.2087 | 4.88 | 3050 | 0.5409 | 0.84 | | 0.1612 | 4.96 | 3100 | 0.5410 | 0.84 | | e8497edbccf895ba19f383b69a5d870b |
mit | [] | false | retropixelart pinguin on Stable Diffusion This is the `<retropixelart-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:         | 60cd2bc9bcca2e6d5bb58075934dd212 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_unispeech_s870 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](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 fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | d3e370d8bf483145b6e09963fa538398 |
cc-by-sa-4.0 | ['coptic', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pre-trained with [UD_Coptic](https://universaldependencies.org/cop/) for POS-tagging and dependency-parsing, derived from [deberta-base-coptic](https://huggingface.co/KoichiYasuoka/deberta-base-coptic). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech). | ca83a729975f148b9c5370cda92a1624 |
cc-by-sa-4.0 | ['coptic', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-coptic-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-coptic-upos") ``` or ``` import esupar nlp=esupar.load("KoichiYasuoka/deberta-base-coptic-upos") ``` | f11023d4db0b4e9d0242e3a9a599a3ba |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_xlsr-53_s533 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](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 fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 643eba29a16d7accd9387069a4621101 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the shiba concept trained by ashiqabdulkhader on the ashiqabdulkhader/animals dataset. This is a Stable Diffusion model fine-tuned on the shiba concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of shiba dog** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! | 2c6d2f68dae9e9ebbcc808642226b491 |
apache-2.0 | ['translation'] | false | opus-mt-he-sv * source languages: he * target languages: sv * OPUS readme: [he-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/he-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/he-sv/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/he-sv/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/he-sv/opus-2020-01-09.eval.txt) | a30a63ff9b3d420c9d1135e462ff2f20 |
afl-3.0 | [] | false | Model Description We release all models introduced in our [paper](https://arxiv.org/pdf/2206.11147.pdf), covering 13 different application scenarios. Each model contains 11 billion parameters. | Model | Description | Recommended Application | ----------- | ----------- |----------- | | **rst-all-11b** | **Trained with all the signals below except signals that are used to train Gaokao models** | **All applications below （specialized models are recommended first if high performance is preferred）** | | rst-fact-retrieval-11b | Trained with the following signals: WordNet meaning, WordNet part-of-speech, WordNet synonym, WordNet antonym, wikiHow category hierarchy, Wikidata relation, Wikidata entity typing, Paperswithcode entity typing | Knowledge intensive tasks, information extraction tasks,factual checker | | rst-summarization-11b | Trained with the following signals: DailyMail summary, Paperswithcode summary, arXiv summary, wikiHow summary | Summarization or other general generation tasks, meta-evaluation (e.g., BARTScore) | | rst-temporal-reasoning-11b | Trained with the following signals: DailyMail temporal information, wikiHow procedure | Temporal reasoning, relation extraction, event-based extraction | | rst-information-extraction-11b | Trained with the following signals: Paperswithcode entity, Paperswithcode entity typing, Wikidata entity typing, Wikidata relation, Wikipedia entity | Named entity recognition, relation extraction and other general IE tasks in the news, scientific or other domains| | rst-intent-detection-11b | Trained with the following signals: wikiHow goal-step relation | Intent prediction, event prediction | | rst-topic-classification-11b | Trained with the following signals: DailyMail category, arXiv category, wikiHow text category, Wikipedia section title | general text classification | | rst-word-sense-disambiguation-11b | Trained with the following signals: WordNet meaning, WordNet part-of-speech, WordNet synonym, WordNet antonym | Word sense disambiguation, part-of-speech tagging, general IE tasks, common sense reasoning | | rst-natural-language-inference-11b | Trained with the following signals: ConTRoL dataset, DREAM dataset, LogiQA dataset, RACE & RACE-C dataset, ReClor dataset, DailyMail temporal information | Natural language inference, multiple-choice question answering, reasoning | | rst-sentiment-classification-11b | Trained with the following signals: Rotten Tomatoes sentiment, Wikipedia sentiment | Sentiment classification, emotion classification | | rst-gaokao-rc-11b | Trained with multiple-choice QA datasets that are used to train the [T0pp](https://huggingface.co/bigscience/T0pp) model | General multiple-choice question answering| | rst-gaokao-cloze-11b | Trained with manually crafted cloze datasets | General cloze filling| | rst-gaokao-writing-11b | Trained with example essays from past Gaokao-English exams and grammar error correction signals | Essay writing, story generation, grammar error correction and other text generation tasks | | a32c73a6f04d337ed1ead80250f5ba3e |
apache-2.0 | ['Early Modern French', 'Historical'] | false | D'AlemBERT base model This model is a [RoBERTa base model](https://huggingface.co/bert-base-uncased) pre-trained on the [FreEMmax corpus](https://doi.org/10.5281/zenodo.6481135) for Early Modern French. It was introduced in [this paper](https://aclanthology.org/2022.lrec-1.359/). This model is Cased and was trained with a mix of normalized and unnormalized data. | dd17fd1e3c69d65339eb1b84c5184989 |
apache-2.0 | ['Early Modern French', 'Historical'] | false | Model description D'AlemBERT is a transformers mode pretrained on the raw texts only with no humans labelling them in any way with an automatic process to generate inputs and labels from those texts using the RoBERTa base model. More precisely, it was pretrained with one objective: - Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. | 44a26f39051dcc46ec418dd4a8c6b212 |
apache-2.0 | ['Early Modern French', 'Historical'] | false | Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT. The model is primarily intended for use in Digital Humanities and Historical NLP. | bcad88e2f148e7369aa93ed71f4ebb0b |
apache-2.0 | ['Early Modern French', 'Historical'] | false | Limitations and bias This model is trained with historical French data from starting from the 16th c., so it might produce results that seem extremely biased by today standards. It might not work well on contemporary data and it is not intended to be used on it. This bias will also affect all fine-tuned versions of this model. | c63b19231016d670325555149333893e |
apache-2.0 | ['Early Modern French', 'Historical'] | false | Training data D'AlemBERT was pretrained on the non-freely available version of the [FreEMmax corpus](https://doi.org/10.5281/zenodo.6481135), a dataset consisting of more than 180k tokens coming from 22 different sources, and comprising French textual data going from the 16th c to the early 20th c. | 4dbaa8e630a186a58374bd4fc9ca3fab |
apache-2.0 | ['Early Modern French', 'Historical'] | false | BibTeX entry and citation info ```bibtex @inproceedings{gabay-etal-2022-freem, title = "From {F}re{EM} to D{'}{A}lem{BERT}: a Large Corpus and a Language Model for Early {M}odern {F}rench", author = "Gabay, Simon and Ortiz Suarez, Pedro and Bartz, Alexandre and Chagu{\'e}, Alix and Bawden, Rachel and Gambette, Philippe and Sagot, Beno{\^\i}t", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.359", pages = "3367--3374", abstract = "anguage models for historical states of language are becoming increasingly important to allow the optimal digitisation and analysis of old textual sources. Because these historical states are at the same time more complex to process and more scarce in the corpora available, this paper presents recent efforts to overcome this difficult situation. These efforts include producing a corpus, creating the model, and evaluating it with an NLP task currently used by scholars in other ongoing projects.", } ``` | 7887192756d13a5dddc88ce50bafca99 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-TIMIT-IPA2 This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1531 - Per: 0.0638 | ab8ffedfb6319422f3832e27b5b3bba2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Per | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.0846 | 6.85 | 500 | 0.1810 | 0.0991 | | 0.1857 | 13.7 | 1000 | 0.1411 | 0.0691 | | 0.0948 | 20.55 | 1500 | 0.1345 | 0.0666 | | 0.0646 | 27.4 | 2000 | 0.1444 | 0.0673 | | 0.0502 | 34.25 | 2500 | 0.1436 | 0.0628 | | 0.0381 | 41.1 | 3000 | 0.1383 | 0.0637 | | 0.0309 | 47.95 | 3500 | 0.1533 | 0.0638 | | 0.0261 | 54.79 | 4000 | 0.1531 | 0.0638 | | 9c07290c9593927b298d42263ab64f91 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | The embeddings in this repository were trained for the 768px [Stable Diffusion v2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1) model. The embeddings should work on any model that uses SD v2.1 as a base. **Examples** <div align="center"> <img src="https://huggingface.co/ProGamerGov/winter-cat-embeddings-sd-v2-1/resolve/main/v1_size_768_t3x3_1.png"> </div> * [Full Image](https://huggingface.co/ProGamerGov/winter-cat-embeddings-sd-v2-1/resolve/main/v1_size_768_t3x3_1.png) <div align="center"> <img src="https://huggingface.co/ProGamerGov/winter-cat-embeddings-sd-v2-1/resolve/main/v1_size_768_t3x3_2.png"> </div> * [Full Image](https://huggingface.co/ProGamerGov/winter-cat-embeddings-sd-v2-1/resolve/main/v1_size_768_t3x3_2.png) <div align="center"> <img src="https://huggingface.co/ProGamerGov/winter-cat-embeddings-sd-v2-1/resolve/main/v1_size_768_t3x3_3.png"> </div> * [Full Image](https://huggingface.co/ProGamerGov/winter-cat-embeddings-sd-v2-1/resolve/main/v1_size_768_t3x3_3.png) <div align="center"> <img src="https://huggingface.co/ProGamerGov/winter-cat-embeddings-sd-v2-1/resolve/main/v1_size_768_t3x3_4.png"> </div> * [Full Image](https://huggingface.co/ProGamerGov/winter-cat-embeddings-sd-v2-1/resolve/main/v1_size_768_t3x3_4.png) **Usage** To use the embeddings, download and then rename the files to whatever trigger word you want to use or just keep the original name. Example Prompt: ``` a cute skinny cat wearing a winter toque and scarf in the snow, realistic, at artstation and behance, art by wcat8-v2-2000, cinematic lighting, night, heavy snowfall, snowstorm ``` Negative Prompt: ``` blurry, photo, smooth, cartoon animal, vector art, 2d, illustration, two deer wearing suits, comic book, 1970 film photography, very sexy woman with black hair, red on black, lace dress, deformed, tokyo japan, pen drawing, fat, chubby, concept art ``` | b17462fc1dd588761b297d6adf0bf596 |
cc-by-4.0 | ['questions and answers generation'] | false | Model Card of `lmqg/t5-small-squad-qag` This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question & answer pair generation task on the [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | 776fa0ac83ab96569b6b2524ef514099 |
cc-by-4.0 | ['questions and answers generation'] | false | Overview - **Language model:** [t5-small](https://huggingface.co/t5-small) - **Language:** en - **Training data:** [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) | 0354c0ab203b77ba26452de5d76b961b |
cc-by-4.0 | ['questions and answers generation'] | false | model prediction question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-small-squad-qag") output = pipe("generate question and answer: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` | b559bfe7cc284d6e51f86826019511a4 |
cc-by-4.0 | ['questions and answers generation'] | false | Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-small-squad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_squad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 92.76 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedF1Score (MoverScore) | 64.59 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedPrecision (BERTScore) | 92.87 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedPrecision (MoverScore) | 65.3 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedRecall (BERTScore) | 92.68 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedRecall (MoverScore) | 63.99 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | 85b455aeecabe8180689a071d2dd6dd9 |
cc-by-4.0 | ['questions and answers generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_squad - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: ['qag'] - model: t5-small - max_length: 512 - max_length_output: 256 - epoch: 18 - batch: 32 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 2 - label_smoothing: 0.0 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-small-squad-qag/raw/main/trainer_config.json). | fd0406c57d11f3842c2308895e765627 |
apache-2.0 | ['generated_from_keras_callback'] | false | my-finetuned-distilbert 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: 1.6482 - Validation Loss: 1.3103 - Epoch: 0 | b6c50e9b64af6fafb182b80f58c2a932 |
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': 1500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 | 39ca7ca3cf88121a24fa42c3746294e0 |
mit | ['generated_from_trainer'] | false | camembert-ner This model is a fine-tuned version of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1179 - Overall Precision: 0.7367 - Overall Recall: 0.7522 - Overall F1: 0.7444 - Overall Accuracy: 0.9728 - Humanprod F1: 0.1639 - Loc F1: 0.7657 - Org F1: 0.5352 - Per F1: 0.7961 | 1da3ca293c14219b3819f3e884856abc |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Humanprod F1 | Loc F1 | Org F1 | Per F1 | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------------:|:------:|:------:|:------:| | No log | 1.0 | 307 | 0.1254 | 0.7185 | 0.7420 | 0.7300 | 0.9715 | 0.0357 | 0.7579 | 0.5052 | 0.7778 | | 0.1195 | 2.0 | 614 | 0.1179 | 0.7367 | 0.7522 | 0.7444 | 0.9728 | 0.1639 | 0.7657 | 0.5352 | 0.7961 | | 2f3a577215c859a52a8893c9c05b122e |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_gender_male-5_female-5_s73 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](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 fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 63055a1677c85e10c5ba48dc8862346e |
apache-2.0 | ['generated_from_trainer'] | false | T5-model-1-feedback-0510 This model is a fine-tuned version of [theojolliffe/T5-model-1-feedback-1109](https://huggingface.co/theojolliffe/T5-model-1-feedback-1109) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2334 - Rouge1: 91.6115 - Rouge2: 86.7084 - Rougel: 91.0616 - Rougelsum: 91.1197 - Gen Len: 14.7895 | e31f5845ead9c98a7ce7aa852554dd0c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.4381 | 1.0 | 542 | 0.2731 | 90.324 | 84.2616 | 89.0178 | 89.1459 | 14.5614 | | 0.2999 | 2.0 | 1084 | 0.2374 | 91.6458 | 86.2909 | 90.8275 | 90.8257 | 14.7719 | | 0.273 | 3.0 | 1626 | 0.2382 | 91.4445 | 86.8218 | 91.1231 | 91.0886 | 14.8947 | | 0.2248 | 4.0 | 2168 | 0.2334 | 91.6115 | 86.7084 | 91.0616 | 91.1197 | 14.7895 | | 047fafd6b8f398cf624c5f5ff748a050 |
apache-2.0 | ['timm', 'vision'] | false | Model Details The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within. This instance of the CLIP model is intended for loading in * `timm` (https://github.com/rwightman/pytorch-image-models) and * `OpenCLIP` (https://github.com/mlfoundations/open_clip) libraries. Please see https://huggingface.co/openai/clip-vit-base-patch32 for use in Hugging Face Transformers. | dea7ffb2cfea543f469565c5e175f404 |
apache-2.0 | ['timm', 'vision'] | false | Model Type The model uses a ViT-B/32 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. The original implementation had two variants: one using a ResNet image encoder and the other using a Vision Transformer. This repository has the variant with the Vision Transformer. | 29b1ddda620f47a6b6713d5b311b3759 |
apache-2.0 | ['timm', 'vision'] | false | Intended Use The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. | c2181220578ed4f63d0c8d7f9a6c4c23 |
apache-2.0 | ['timm', 'vision'] | false | Primary intended uses The primary intended users of these models are AI researchers. We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models. | 52812f7de1c8ca1b7f3ce849b35b68fd |
apache-2.0 | ['timm', 'vision'] | false | Out-of-Scope Use Cases **Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use. Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases. | 91de8001632f1ee6ce944737616e0cd2 |
apache-2.0 | ['timm', 'vision'] | false | Data The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users. | 507234f10751679d596ec75392ffa6b6 |
apache-2.0 | ['timm', 'vision'] | false | Data Mission Statement Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset. | 7724f314b1dab207e83d0207db56f1cb |
apache-2.0 | ['timm', 'vision'] | false | Limitations CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance. | e17865921fcd0f8f0734716c60dc31ef |
apache-2.0 | ['timm', 'vision'] | false | Bias and Fairness We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper). We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with ‘Middle Eastern’ having the highest accuracy (98.4%) and ‘White’ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks. | f663de7dbfc583d4f575a0ba4587de7b |
mit | ['generated_from_trainer'] | false | deberta-classifier-feedback-1024-pseudo-final This model is a fine-tuned version of [TTian/deberta-classifier-feedback-1024-pseudo](https://huggingface.co/TTian/deberta-classifier-feedback-1024-pseudo) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5263 | 88c569917cb592837b885ed2767e5f81 |
mit | ['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 - 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 - num_epochs: 2 - mixed_precision_training: Native AMP | 2564c0e46e4e16855c09bb0bffc2e2ac |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5814 | 0.04 | 10 | 0.5888 | | 0.5521 | 0.08 | 20 | 0.5736 | | 0.5685 | 0.13 | 30 | 0.5809 | | 0.6052 | 0.17 | 40 | 0.5702 | | 0.5532 | 0.21 | 50 | 0.5571 | | 0.6177 | 0.25 | 60 | 0.5848 | | 0.6196 | 0.3 | 70 | 0.5464 | | 0.5772 | 0.34 | 80 | 0.5307 | | 0.5805 | 0.38 | 90 | 0.5550 | | 0.6453 | 0.42 | 100 | 0.5467 | | 0.5756 | 0.47 | 110 | 0.5587 | | 0.5901 | 0.51 | 120 | 0.5482 | | 0.568 | 0.55 | 130 | 0.5263 | | 0.5452 | 0.59 | 140 | 0.5698 | | 0.5949 | 0.64 | 150 | 0.5484 | | 0.5537 | 0.68 | 160 | 0.5783 | | 0.5327 | 0.72 | 170 | 0.5202 | | 0.5449 | 0.76 | 180 | 0.5272 | | 0.5345 | 0.81 | 190 | 0.5621 | | 0.5837 | 0.85 | 200 | 0.5501 | | 0.5969 | 0.89 | 210 | 0.5470 | | 0.5905 | 0.93 | 220 | 0.5924 | | 0.5481 | 0.97 | 230 | 0.5415 | | 0.5035 | 1.02 | 240 | 0.5321 | | 0.4508 | 1.06 | 250 | 0.5371 | | 0.4227 | 1.1 | 260 | 0.5276 | | 0.4423 | 1.14 | 270 | 0.5324 | | 0.432 | 1.19 | 280 | 0.5378 | | 0.4317 | 1.23 | 290 | 0.5302 | | 0.46 | 1.27 | 300 | 0.5302 | | 0.435 | 1.31 | 310 | 0.5326 | | 0.3813 | 1.36 | 320 | 0.5431 | | 0.4422 | 1.4 | 330 | 0.5323 | | 0.4298 | 1.44 | 340 | 0.5575 | | 0.5068 | 1.48 | 350 | 0.5529 | | 0.4619 | 1.53 | 360 | 0.5589 | | 0.4852 | 1.57 | 370 | 0.5256 | | 0.3888 | 1.61 | 380 | 0.5731 | | 0.4319 | 1.65 | 390 | 0.5335 | | 0.4422 | 1.69 | 400 | 0.5419 | | 0.4522 | 1.74 | 410 | 0.5547 | | 0.4276 | 1.78 | 420 | 0.5263 | | 0.3988 | 1.82 | 430 | 0.5481 | | 0.4063 | 1.86 | 440 | 0.5404 | | 0.4141 | 1.91 | 450 | 0.5292 | | 0.4149 | 1.95 | 460 | 0.5241 | | 0.4104 | 1.99 | 470 | 0.5263 | | 92c908a97dab96d12006b0f21e960579 |
apache-2.0 | ['generated_from_keras_callback'] | false | fintuned-bert-disfluency 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: 0.0814 - Train Sparse Categorical Accuracy: 0.9795 - Validation Loss: 0.0816 - Validation Sparse Categorical Accuracy: 0.9795 - Epoch: 2 | 4059b2968a353d3ecc5f2fa4824b2c01 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch | |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:| | 0.1105 | 0.9694 | 0.0821 | 0.9800 | 0 | | 0.0942 | 0.9759 | 0.0987 | 0.9765 | 1 | | 0.0814 | 0.9795 | 0.0816 | 0.9795 | 2 | | 7341b36b471a4c70be93de6e19d2e4f8 |
apache-2.0 | ['generated_from_trainer'] | false | convnext-tiny-224-eurosat This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3153 - Accuracy: 0.9537 | b6be9ae7385343ae0820bb5a9e418648 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.863 | 0.98 | 33 | 1.5775 | 0.7619 | | 1.039 | 1.98 | 66 | 0.8142 | 0.9008 | | 0.5825 | 2.98 | 99 | 0.4442 | 0.9339 | | 0.3228 | 3.98 | 132 | 0.3153 | 0.9537 | | 0.2641 | 4.98 | 165 | 0.2868 | 0.9524 | | cf7504f6fb9b5386634aaeb7f69201b8 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | eduardosflopes2 Dreambooth model trained by eduardosflopes 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-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept: | d4121df397ed8d1280bbcfba7740a520 |
mit | ['generated_from_trainer'] | false | vit-swin-base-224-gpt2-image-captioning This model is a fine-tuned [VisionEncoderDecoder](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder) model on 60% of the [COCO2014](https://huggingface.co/datasets/HuggingFaceM4/COCO) dataset. It achieves the following results on the testing set: - Loss: 0.7989 - Rouge1: 53.1153 - Rouge2: 24.2307 - Rougel: 51.5002 - Rougelsum: 51.4983 - Bleu: 17.7765 | 094590cef1c0068a0c90c915fe883598 |
mit | ['generated_from_trainer'] | false | Model description The model was initialized on [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) as the vision encoder, the [gpt2](https://huggingface.co/gpt2) as the decoder. | 7bbe07c5dc9f6a40dce06986e3399fd5 |
mit | ['generated_from_trainer'] | false | How to use You can either use the simple pipeline API: ```python from transformers import pipeline image_captioner = pipeline("image-to-text", model="Abdou/vit-swin-base-224-gpt2-image-captioning") | 1e700ba771780a6072ae9d247145c833 |
mit | ['generated_from_trainer'] | false | infer the caption caption = image_captioner("http://images.cocodataset.org/test-stuff2017/000000000019.jpg")[0]['generated_text'] print(f"caption: {caption}") ``` Or initialize everything for more flexibility: ```python from transformers import VisionEncoderDecoderModel, GPT2TokenizerFast, ViTImageProcessor import torch | ec7a30691bf00b98cba278fce5ca9e36 |
mit | ['generated_from_trainer'] | false | load the fine-tuned image captioning model and corresponding tokenizer and image processor model = VisionEncoderDecoderModel.from_pretrained("Abdou/vit-swin-base-224-gpt2-image-captioning").to(device) tokenizer = GPT2TokenizerFast.from_pretrained("Abdou/vit-swin-base-224-gpt2-image-captioning") image_processor = ViTImageProcessor.from_pretrained("Abdou/vit-swin-base-224-gpt2-image-captioning") | 116abe12f283e1f36c53898538c6dd2c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:| | 1.0018 | 0.38 | 2000 | 0.8860 | 38.6537 | 13.8145 | 35.3932 | 35.3935 | 8.2448 | 11.2946 | | 0.8827 | 0.75 | 4000 | 0.8395 | 40.0458 | 14.8829 | 36.5321 | 36.5366 | 9.1169 | 11.2946 | | 0.8378 | 1.13 | 6000 | 0.8140 | 41.2736 | 15.9576 | 37.5504 | 37.5512 | 9.871 | 11.2946 | | 0.7913 | 1.51 | 8000 | 0.8012 | 41.6642 | 16.1987 | 37.8786 | 37.8891 | 10.0786 | 11.2946 | | 0.7794 | 1.89 | 10000 | 0.7933 | 41.9119 | 16.3738 | 38.1062 | 38.1292 | 10.288 | 11.2946 | Total training time: ~5 hours on NVIDIA A100 GPU. | 09f26cab4e046ac4001d52975459e068 |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-imdb This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.1783 - Accuracy: 0.9552 | ecb1b7c6a993c5f7f6307100615a61c1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1904 | 1.0 | 1563 | 0.1423 | 0.9517 | | 0.1187 | 2.0 | 3126 | 0.1783 | 0.9552 | | aff50e9505ed74116859a0bab01d1fc3 |
apache-2.0 | ['Image Captioning'] | false | Model Description These are model weights originally provided by the authors of the paper [Text-Only Training for Image Captioning using Noise-Injected CLIP](https://arxiv.org/pdf/2211.00575.pdf). Their method aims to train CLIP with only text samples. Therefore they are injecting zero-mean Gaussian Noise into the text embeddings before decoding. In their words: *Specifically, we assume that the visual embedding corresponding to a text embedding lies somewhere within a ball of small radius around the text embedding (see Fig. 1). We would like all text embeddings in this ball to decode to the same caption,which should also correspond to the visual content mapped to this ball. We implement this intuition by adding zero-mean Gaussian noise of STD to the text embedding before decoding it.* The "Noise Level" of 0 is equivalent to the Noise Variance which is the square of the STD. The reported metrics are results of a model with a Noise Variance of 0.016, which the authors unfortunately do not provide in their repository. | 4e0236c0a357360536a6fd88b0ca866d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 367 | 0.4436 | 0.8106 | 0.8597 | | 60b6ff1144a51599ae56933f55b629f1 |
mit | [] | false | German GPT-2 model In this repository we release (yet another) GPT-2 model, that was trained on various texts for German. The model is meant to be an entry point for fine-tuning on other texts, and it is definitely not as good or "dangerous" as the English GPT-3 model. We do not plan extensive PR or staged releases for this model 😉 **Note**: The model was initially released under an anonymous alias (`anonymous-german-nlp/german-gpt2`) so we now "de-anonymize" it. More details about GPT-2 can be found in the great [Hugging Face](https://huggingface.co/transformers/model_doc/gpt2.html) documentation. | 7ab16dbc3f8f1e0fd5b6672413acd740 |
mit | [] | false | Changelog 16.08.2021: Public release of re-trained version of our German GPT-2 model with better results. 15.11.2020: Initial release. Please use the tag `v1.0` for [this older version](https://huggingface.co/dbmdz/german-gpt2/tree/v1.0). | c0d5845a63ad1d642e5cd1608a4578ef |
mit | [] | false | Training corpora We use pretty much the same corpora as used for training the DBMDZ BERT model, that can be found in [this repository](https://github.com/dbmdz/berts). Thanks to the awesome Hugging Face team, it is possible to create byte-level BPE with their awesome [Tokenizers](https://github.com/huggingface/tokenizers) library. With the previously mentioned awesome Tokenizers library we created a 50K byte-level BPE vocab based on the training corpora. After creating the vocab, we could train the GPT-2 for German on a v3-8 TPU over the complete training corpus for 20 epochs. All hyperparameters can be found in the official JAX/FLAX documentation [here](https://github.com/huggingface/transformers/blob/master/examples/flax/language-modeling/README.md) from Transformers. | 7727af875f28eed4e7775d21e1997387 |
mit | [] | false | Using the model The model itself can be used in this way: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("dbmdz/german-gpt2") model = AutoModelWithLMHead.from_pretrained("dbmdz/german-gpt2") ``` However, text generation is a bit more interesting, so here's an example that shows how to use the great Transformers *Pipelines* for generating text: ```python from transformers import pipeline pipe = pipeline('text-generation', model="dbmdz/german-gpt2", tokenizer="dbmdz/german-gpt2") text = pipe("Der Sinn des Lebens ist es", max_length=100)[0]["generated_text"] print(text) ``` This could output this beautiful text: ``` Der Sinn des Lebens ist es, im Geist zu verweilen, aber nicht in der Welt zu sein, sondern ganz im Geist zu leben. Die Menschen beginnen, sich nicht nach der Natur und nach der Welt zu richten, sondern nach der Seele,' ``` | 57c792c082bd230cac991445be12ea38 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr2e05-wd0.01-bs16 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: 0.2792 - Rmse: 0.5284 - Mse: 0.2792 - Mae: 0.4332 | 044120fb9f3e0a26807b14825f7a6638 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2768 | 1.0 | 1245 | 0.2747 | 0.5241 | 0.2747 | 0.4081 | | 0.2737 | 2.0 | 2490 | 0.2793 | 0.5285 | 0.2793 | 0.4288 | | 0.2722 | 3.0 | 3735 | 0.2792 | 0.5284 | 0.2792 | 0.4332 | | b248b98b07ef87942acb6b026f0a6587 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | deployment-with-nvidia-riva) | This model transcribes speech in lower case English alphabet along with spaces and apostrophes. It is an "extra-small" versions of Citrinet-CTC (around 10M parameters) model. See the [model architecture]( | 3fce83434442217daa6a1f4841997ebc |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/stt_en_citrinet_256_ls" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` | 0e1aef9fc9183dd1eade5972d29163fc |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Model Architecture Citrinet-CTC model is an autoregressive variant of Citrinet model [1] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer Loss. You may find more info on the detail of this model here: [Citrinet Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html). | 324289f3ed355b6b25f9b094f7a08f01 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Training The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/citrinet/citrinet_1024.yaml) (Note: Change the `model.model_defaults.filters` to match the model size). The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py). | 013387bca0127684b1e5a38b8d5a502b |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Performance The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. | Version | Tokenizer | Vocabulary Size | LS test-other | LS test-clean | |---------|---------------------------|-----------------|---------------|---------------| | 1.0.0 | SentencePiece Unigram [2] | 256 | 9.8 | 3.8 | | 129c564b72efbf392aa1806383e0e5a9 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | References [1] [ Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition](https://arxiv.org/abs/2104.01721) [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece) [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) | 13cf833782b0bf4305f26874c051fd88 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-stsb-target-glue-mrpc This model is a fine-tuned version of [muhtasham/small-mlm-glue-stsb](https://huggingface.co/muhtasham/small-mlm-glue-stsb) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9122 - Accuracy: 0.7598 - F1: 0.8322 | 6113ef813fac065e8bf23bdcc8eda964 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3924 | 4.35 | 500 | 0.8097 | 0.7647 | 0.8416 | | 0.0751 | 8.7 | 1000 | 1.4556 | 0.7574 | 0.8374 | | 0.0294 | 13.04 | 1500 | 1.7098 | 0.7647 | 0.8356 | | 0.0186 | 17.39 | 2000 | 1.9122 | 0.7598 | 0.8322 | | 486c7a645c14b0b9cc9fc5580f01ffd2 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned-mt5-base This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 1.3594 - Bleu: 27.1659 - Gen Len: 43.9575 | 9018152f49f5476c10a7009fe79fa186 |
apache-2.0 | ['generated_from_trainer'] | false | 42 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3109 - Accuracy: 0.9255 | 60bf798f7e2824812aa3690c0c56ae21 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: not_parallel - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 2068d1c7359371e50d974d6f56a43af2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | No log | 1.0 | 2105 | 0.2167 | 0.9232 | | 0.2049 | 2.0 | 4210 | 0.2375 | 0.9278 | | 0.123 | 3.0 | 6315 | 0.2636 | 0.9243 | | 0.0839 | 4.0 | 8420 | 0.2865 | 0.9243 | | 0.058 | 5.0 | 10525 | 0.3109 | 0.9255 | | 009afa575c12c77f6bdb3aece261dcdc |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Model Card of `lmqg/mt5-base-esquad-qg-ae` This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation and answer extraction jointly on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | e4325f344298b661e7ebec5df9505c7e |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | model prediction question_answer_pairs = model.generate_qa("a noviembre , que es también la estación lluviosa.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-base-esquad-qg-ae") | cd44c3dcd3d883323d61c2548f19d8c4 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | question generation question = pipe("extract answers: <hl> En la diáspora somalí, múltiples eventos islámicos de recaudación de fondos se llevan a cabo cada año en ciudades como Birmingham, Londres, Toronto y Minneapolis, donde los académicos y profesionales somalíes dan conferencias y responden preguntas de la audiencia. <hl> El propósito de estos eventos es recaudar dinero para nuevas escuelas o universidades en Somalia, para ayudar a los somalíes que han sufrido como consecuencia de inundaciones y / o sequías, o para reunir fondos para la creación de nuevas mezquitas como.") ``` | 4395793711bfcf7a5c6145f46c19c868 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-esquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 83.97 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_1 | 25.88 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_2 | 17.67 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_3 | 12.84 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_4 | 9.62 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | METEOR | 23.11 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | MoverScore | 59.15 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | ROUGE_L | 24.82 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-esquad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_esquad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 79.67 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedF1Score (MoverScore) | 54.82 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedPrecision (BERTScore) | 77.14 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedPrecision (MoverScore) | 53.27 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedRecall (BERTScore) | 82.44 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedRecall (MoverScore) | 56.56 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-esquad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_esquad.default.json) | | Score | Type | Dataset | |:-----------------|--------:|:--------|:-----------------------------------------------------------------| | AnswerExactMatch | 57.98 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | AnswerF1Score | 75.33 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | BERTScore | 90.04 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_1 | 37.35 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_2 | 32.53 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_3 | 28.86 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_4 | 25.75 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | METEOR | 43.74 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | MoverScore | 80.94 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | ROUGE_L | 49.61 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | 8dc2c18ff85178ee543d39e2dfd83b4d |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_esquad - dataset_name: default - input_types: ['paragraph_answer', 'paragraph_sentence'] - output_types: ['question', 'answer'] - prefix_types: ['qg', 'ae'] - model: google/mt5-base - max_length: 512 - max_length_output: 32 - epoch: 7 - batch: 32 - lr: 0.001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 2 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-esquad-qg-ae/raw/main/trainer_config.json). | 37772f440c07ca6085addc2ff288b902 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_sst2_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.5433 - Accuracy: 0.7878 | 3bdd38f98eb0cdda2dfe842f36b68073 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3436 | 1.0 | 4374 | 0.5433 | 0.7878 | | 0.2417 | 2.0 | 8748 | 0.6281 | 0.7890 | | 0.1823 | 3.0 | 13122 | 0.7529 | 0.7775 | | 0.1432 | 4.0 | 17496 | 0.8767 | 0.7741 | | 0.117 | 5.0 | 21870 | 0.9864 | 0.7638 | | 0.0986 | 6.0 | 26244 | 1.1162 | 0.7649 | | 41febc490dddf59d3a44b659c56d555e |
mit | ['generated_from_keras_callback'] | false | syp1229/xlm-roberta-base-finetuned-koidiom-epoch5 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.0826 - Validation Loss: 1.9873 - Epoch: 4 | 377c9ad8dcd249ef18544970c65250dd |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.7703 | 2.0462 | 0 | | 2.2504 | 2.0178 | 1 | | 2.1653 | 1.9992 | 2 | | 2.1310 | 1.9829 | 3 | | 2.0826 | 1.9873 | 4 | | bbab20e1a78b38355b2083de731bc1f7 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 91325a1e58ca0b13494b94bf79b186b095fe0b58 pip install -e . cd egs2/mr_openslr64/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/marathi_openslr64 ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 5d1d8608c5a2747fd75a46fe8ad641da |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Mon Mar 21 16:06:03 UTC 2022` - python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.11.0+cu102` - Git hash: `91325a1e58ca0b13494b94bf79b186b095fe0b58` - Commit date: `Mon Mar 21 00:40:52 2022 +0000` | ea6a49ece7a8dd70d3dbff57cbc16c00 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_xlsr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_xlsr_raw_bpe150_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 60 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 5 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 3 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: - frontend.upstream num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 10000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_bpe150_sp/train/speech_shape - exp/asr_stats_raw_bpe150_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_bpe150_sp/valid/speech_shape - exp/asr_stats_raw_bpe150_sp/valid/text_shape.bpe batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/marathi_train_sp/wav.scp - speech - sound - - dump/raw/marathi_train_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/marathi_dev/wav.scp - speech - sound - - dump/raw/marathi_dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.0005 scheduler: warmuplr scheduler_conf: warmup_steps: 20000 token_list: - <blank> - <unk> - ▁ - ा - ी - े - त - र - ं - न - क - ् - व - ि - ल - ▁म - स - ो - श - द - च - म - ▁अ - ▁आ - ण - ु - ला - ह - ▁आहे - य - ▁स - ग - ▁ह - ्या - चा - ▁प - ड - ▁क - प - ट - ▁ब - ज - र् - ्र - ▁? - ▁ज - ब - ून - वा - ▁एक - ▁या - ळ - ात - ख - ध - ▁ति - ठ - ल्या - ले - ू - ▁तुम्हाला - ां - ार - घ - ची - ▁अस - थ - ▁का - ने - णि - ॅ - ▁त - ▁परवा - ▁ते - ली - ▁गेल - ळा - ष - ▁कर - . - च्या - ▁न - वर - ▁त्या - ▁प्र - ▁करू - ▁ग - ्ट - ई - झ - ▁फ - ाय - क्ष - ▁काय - पूर - ▁होती - मध - ▁तिथ - ▁काही - ए - ▁वि - ▁दोन - ▁महिन्या - व्हा - तील - जार - ▁नाही - ँ - ▁पुत - ॉ - ▁झाला - ▁दिसल - ▁साल - ▁रस्त्यावर - स्त - जवळ - न्म - मध्य - ऊ - ▁इथे - ▁तुमच - ▁शकते - मान - ▁उद् - फ - ै - ढ - ',' - इ - ौ - - ृ - ओ - ः - ॲ - आ - '-' - ञ - औ - '!' - ऑ - ऱ - ऐ - छ - उ - '?' - भ - अ - ऋ - <sos/eos> init: xavier_uniform input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true joint_net_conf: null use_preprocessor: true token_type: bpe bpemodel: data/token_list/bpe_unigram150/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: s3prl frontend_conf: frontend_conf: upstream: wav2vec2_xlsr download_dir: ./hub multilayer_feature: true fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: utterance_mvn normalize_conf: {} model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false extract_feats_in_collect_stats: false preencoder: linear preencoder_conf: input_size: 1024 output_size: 80 encoder: conformer encoder_conf: output_size: 512 attention_heads: 4 linear_units: 1024 num_blocks: 3 dropout_rate: 0.3 positional_dropout_rate: 0.3 attention_dropout_rate: 0.3 input_layer: conv2d normalize_before: true macaron_style: false pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 17 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 4 linear_units: 1024 num_blocks: 3 dropout_rate: 0.3 positional_dropout_rate: 0.3 self_attention_dropout_rate: 0.3 src_attention_dropout_rate: 0.3 required: - output_dir - token_list version: 0.10.7a1 distributed: false ``` </details> | 047b9f92fb1d0fa1655b863a5423218a |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | MultiBERTs Seed 2 Checkpoint 20k (uncased) Seed 2 intermediate checkpoint 20k 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/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-2](https://hf.co/multberts-seed-2). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). | 69de28d8981a610d4a3c3ab5e79a1243 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-2-20k') model = BertModel.from_pretrained("multiberts-seed-2-20k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 14cdc93506754089a7c5853d75df476b |
mit | ['generated_from_trainer'] | false | upbeat_ramanujan This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. | 4595b95ccccb6453e5e00ed967c58cee |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'every_n_steps': 16, 'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'every_n_steps': 16, 'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'value_head_config': {'is_detached': False}}, 'path_or_name': 'gpt2'}, 'objective': {'alpha': 0.5, 'beta': 10, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 1024, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'upbeat_ramanujan', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.001, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 1673, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | f54e02aea9ec8c71ba475bb22028efad |
apache-2.0 | ['translation'] | false | eng-phi * source group: English * target group: Philippine languages * OPUS readme: [eng-phi](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-phi/README.md) * model: transformer * source language(s): eng * target language(s): akl_Latn ceb hil ilo pag war * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-phi/opus2m-2020-08-01.zip) * test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-phi/opus2m-2020-08-01.test.txt) * test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-phi/opus2m-2020-08-01.eval.txt) | 87055bcd1c0101261a3bc9f7f41de984 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.eng-akl.eng.akl | 7.1 | 0.245 | | Tatoeba-test.eng-ceb.eng.ceb | 10.5 | 0.435 | | Tatoeba-test.eng-hil.eng.hil | 18.0 | 0.506 | | Tatoeba-test.eng-ilo.eng.ilo | 33.4 | 0.590 | | Tatoeba-test.eng.multi | 13.1 | 0.392 | | Tatoeba-test.eng-pag.eng.pag | 19.4 | 0.481 | | Tatoeba-test.eng-war.eng.war | 12.8 | 0.441 | | 93140539840f60ecda857204ecccd63f |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-phi - source_languages: eng - target_languages: phi - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-phi/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'phi'] - src_constituents: {'eng'} - tgt_constituents: {'ilo', 'akl_Latn', 'war', 'hil', 'pag', 'ceb'} - src_multilingual: False - tgt_multilingual: True - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-phi/opus2m-2020-08-01.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-phi/opus2m-2020-08-01.test.txt - src_alpha3: eng - tgt_alpha3: phi - short_pair: en-phi - chrF2_score: 0.392 - bleu: 13.1 - brevity_penalty: 1.0 - ref_len: 30022.0 - src_name: English - tgt_name: Philippine languages - train_date: 2020-08-01 - src_alpha2: en - tgt_alpha2: phi - prefer_old: False - long_pair: eng-phi - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 15df51a482aa9d381c9a6404212c97d8 |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'capitel', 'ner'] | false | Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset. RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne | e54ca0228e2f50efa5ab5bf270f0a988 |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'capitel', 'ner'] | false | Dataset The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 1). **IMPORTANT ABOUT THIS MODEL:** We modified the dataset to make this model more robust to general Spanish input. In the Spanish language all the name entities are capitalized, as this dataset has been elaborated by experts, it is totally correct in terms of Spanish language. We randomly took some entities and we lower-cased some of them for the model to learn not only that the named entities are capitalized, but also the structure of a sentence that should contain a named entity. For instance: "My name is [placeholder]", this [placeholder] should be a named entity even though it is not written capitalized. The model trained on the original capitel dataset can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne-capitel-ner Examples: This model: - "Me llamo asier y vivo en barcelona todo el año." → "Me llamo {as:S-PER}{ier:S-PER} y vivo en {barcelona:S-LOC} todo el año." - "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." → "Hoy voy a visitar el {par:B-LOC}{k:I-LOC} {gü:E-LOC}{ell:E-LOC} tras salir del {barcelona:B-ORG} {super:I-ORG}{com:I-ORG}{pu:I-ORG}{ting:I-ORG} {cen:E-ORG}{ter:E-ORG}." Model trained on original data: - "Me llamo asier y vivo en barcelona todo el año." → "Me llamo asier y vivo en barcelona todo el año." (nothing) - "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." → "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." (nothing) | 9dc3ce3683d6fef4a6337b516d9e824d |
apache-2.0 | ['translation'] | false | opus-mt-de-loz * source languages: de * target languages: loz * OPUS readme: [de-loz](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-loz/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/de-loz/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-loz/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-loz/opus-2020-01-20.eval.txt) | 8c472a9df25ac8a51a6044ed4781a413 |
apache-2.0 | ['translation'] | false | ara-tur * source group: Arabic * target group: Turkish * OPUS readme: [ara-tur](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-tur/README.md) * model: transformer * source language(s): apc_Latn ara ara_Latn arq_Latn * target language(s): tur * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-tur/opus-2020-07-03.zip) * test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-tur/opus-2020-07-03.test.txt) * test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-tur/opus-2020-07-03.eval.txt) | 7df2d33e9fe154a3b783d2bb3df8e652 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ara-tur - source_languages: ara - target_languages: tur - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-tur/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ar', 'tr'] - src_constituents: {'apc', 'ara', 'arq_Latn', 'arq', 'afb', 'ara_Latn', 'apc_Latn', 'arz'} - tgt_constituents: {'tur'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-tur/opus-2020-07-03.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-tur/opus-2020-07-03.test.txt - src_alpha3: ara - tgt_alpha3: tur - short_pair: ar-tr - chrF2_score: 0.619 - bleu: 33.1 - brevity_penalty: 0.9570000000000001 - ref_len: 6949.0 - src_name: Arabic - tgt_name: Turkish - train_date: 2020-07-03 - src_alpha2: ar - tgt_alpha2: tr - prefer_old: False - long_pair: ara-tur - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 1da36a6b828f98629d5e55a2e044a838 |
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