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 | distilbert-base-zero-shot 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: - eval_loss: 0.7147 - eval_accuracy: 0.0741 - eval_f1: 0.1379 - eval_runtime: 1.1794 - eval_samples_per_second: 22.894 - eval_steps_per_second: 1.696 - step: 0 | e1440b40e4ddea911f90232061da1856 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | MultiBERTs Seed 1 Checkpoint 900k (uncased) Seed 1 intermediate checkpoint 900k 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-1](https://hf.co/multberts-seed-1). 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). | e641b1d8eab0515e75fb6b9467fccf48 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-1-900k') model = BertModel.from_pretrained("multiberts-seed-1-900k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | d8d10effe7079c17532cb3604312172c |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'bas', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-bas-v1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BAS dataset. It achieves the following results on the evaluation set: - Loss: 0.5997 - Wer: 0.3870 | 7e70c334e59ad638b80a2c0456a1c724 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'bas', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-bas-v1 --dataset mozilla-foundation/common_voice_8_0 --config bas --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Basaa (bas) language isn't available in speech-recognition-community-v2/dev_data | 3bd6d6efc6473c504955e5caa6df6864 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'bas', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000111 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100 - mixed_precision_training: Native AMP | 60fb985a0b73ff6bf1c05f2934fa0d52 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'bas', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 12.7076 | 5.26 | 200 | 3.6361 | 1.0 | | 3.1657 | 10.52 | 400 | 3.0101 | 1.0 | | 2.3987 | 15.78 | 600 | 0.9125 | 0.6774 | | 1.0079 | 21.05 | 800 | 0.6477 | 0.5352 | | 0.7392 | 26.31 | 1000 | 0.5432 | 0.4929 | | 0.6114 | 31.57 | 1200 | 0.5498 | 0.4639 | | 0.5222 | 36.83 | 1400 | 0.5220 | 0.4561 | | 0.4648 | 42.1 | 1600 | 0.5586 | 0.4289 | | 0.4103 | 47.36 | 1800 | 0.5337 | 0.4082 | | 0.3692 | 52.62 | 2000 | 0.5421 | 0.3861 | | 0.3403 | 57.88 | 2200 | 0.5549 | 0.4096 | | 0.3011 | 63.16 | 2400 | 0.5833 | 0.3925 | | 0.2932 | 68.42 | 2600 | 0.5674 | 0.3815 | | 0.2696 | 73.68 | 2800 | 0.5734 | 0.3889 | | 0.2496 | 78.94 | 3000 | 0.5968 | 0.3985 | | 0.2289 | 84.21 | 3200 | 0.5888 | 0.3893 | | 0.2091 | 89.47 | 3400 | 0.5849 | 0.3852 | | 0.2005 | 94.73 | 3600 | 0.5938 | 0.3875 | | 0.1876 | 99.99 | 3800 | 0.5997 | 0.3870 | | 4ee71e473e0fe12389cf55fcdf512343 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-basil This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2870 | ee7964e04af582955088f9fd1c64eb93 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9097 | 1.0 | 780 | 1.4978 | | 1.5358 | 2.0 | 1560 | 1.3439 | | 1.4259 | 3.0 | 2340 | 1.2881 | | c6a4d313e4a7e36fc1d4acb15ef47294 |
afl-3.0 | [] | false | reStructured Pre-training (RST) official [repository](https://github.com/ExpressAI/reStructured-Pretraining), [paper](https://arxiv.org/pdf/2206.11147.pdf), [easter eggs](http://expressai.co/peripherals/emoji-eng.html) | 7e31d1445d984b34b3842fd3f026f440 |
afl-3.0 | [] | false | RST is a new paradigm for language pre-training, which * unifies **26** different types of signal from **10** data sources (Totten Tomatoes, Dailymail, Wikipedia, Wikidata, Wikihow, Wordnet, arXiv etc ) in the world structurally, being pre-trained with a monolithcal model, * surpasses strong competitors (e.g., T0) on **52/55** popular datasets from a variety of NLP tasks (classification, IE, retrieval, generation etc) * achieves superior performance in National College Entrance Examination **(Gaokao-English, ้ซ่-่ฑ่ฏญ)** achieves **40** points higher than the average scores made by students and 15 points higher than GPT3 with **1/16** parameters. In particular, Qin gets a high score of **138.5** (the full mark is 150) in the 2018 English exam In such a pre-training paradigm, * Data-centric Pre-training: the role of data will be re-emphasized, and model pre-training and fine-tuning of downstream tasks are viewed as a process of data storing and accessing * Pre-training over JSON instead of TEXT: a good storage mechanism should not only have the ability to cache a large amount of data but also consider the ease of access. | be4f007e56ad8c038241c734aae143bd |
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 ๏ผspecialized models are recommended first if high performance is preferred๏ผ | | 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 | | 68690d82ed93a52891fd44ea6903ae02 |
afl-3.0 | [] | false | Have a try? ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("XLab/rst-all-11b") model = AutoModelForSeq2SeqLM.from_pretrained("XLab/rst-all-11b") inputs = tokenizer.encode("TEXT: this is the best cast iron skillet you will ever buy. QUERY: Is this review \"positive\" or \"negative\"", return_tensors="pt") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)) ``` | 2c5e1813a494c3081d49898e97b9dba7 |
afl-3.0 | [] | false | Data for reStructure Pre-training This dataset is a precious treasure, containing a variety of naturally occurring signals. Any downstream task you can think of (e.g., the college entrance exam mentioned in the RST paper) can benefit from being pre-trained on some of our provided signals. We spent several months collecting the following 29 signal types, accounting for a total of 46,926,447 data samples. We hope this dataset will be a valuable asset for everyone in natural language processing research. We provide collected signals through [DataLab](https://github.com/ExpressAI/DataLab). For efficiency, we only provide 50,000 samples at most for each signal type. If you want all the samples we collected, please fill this [form](https://docs.google.com/forms/d/e/1FAIpQLSdPO50vSdfwoO3D7DQDVlupQnHgrXrwfF3ePE4X1H6BwgTn5g/viewform?usp=sf_link). More specifically, we collected the following signals. | ef561ed7d324e574e4a0c7aa18909e97 |
afl-3.0 | [] | false | Sample | Use in DataLab | Some Applications | | --- | --- | --- | --- | --- | | [Rotten Tomatoes](https://www.rottentomatoes.com/) | (review, rating) | 5,311,109 | `load_dataset("rst", "rotten_tomatoes_sentiment")` | Sentiment classification | | [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (text, category) | 899,904 | `load_dataset("rst", "daily_mail_category")`| Topic classification | | [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (title, text, summary) | 1,026,616 | `load_dataset("rst", "daily_mail_summary")` | Summarization; Sentence expansion| | [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (text, events) | 1,006,412 | `load_dataset("rst", "daily_mail_temporal")` | Temporal reasoning| | [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page) | (entity, entity_type, text) | 2,214,274 | `load_dataset("rst", "wikidata_entity")` | Entity typing| | [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page) | (subject, object, relation, text) | 1,526,674 | `load_dataset("rst", "wikidata_relation")` | Relation extraction; Fact retrieval| | [wikiHow](https://www.wikihow.com/Main-Page) | (text, category) | 112,109 | `load_dataset("rst", "wikihow_text_category")` | Topic classification | | [wikiHow](https://www.wikihow.com/Main-Page) | (low_category, high_category) | 4,868 | `load_dataset("rst", "wikihow_category_hierarchy")` | Relation extraction; Commonsense reasoning| | [wikiHow](https://www.wikihow.com/Main-Page) | (goal, steps) | 47,956 | `load_dataset("rst", "wikihow_goal_step")` | Intent detection| | [wikiHow](https://www.wikihow.com/Main-Page) | (text, summary) | 703,278 | `load_dataset("rst", "wikihow_summary")` | Summarization; Sentence expansion | | [wikiHow](https://www.wikihow.com/Main-Page) | (goal, first_step, second_step) | 47,787 | `load_dataset("rst", "wikihow_procedure")` | Temporal reasoning | | [wikiHow](https://www.wikihow.com/Main-Page) | (question, description, answer, related_questions) | 47,705 | `load_dataset("rst", "wikihow_question")` | Question generation| | [Wikipedia](https://www.wikipedia.org/) | (text, entities) |22,231,011 | `load_dataset("rst", "wikipedia_entities")` | Entity recognition| [Wikipedia](https://www.wikipedia.org/) | (texts, titles) | 3,296,225 | `load_dataset("rst", "wikipedia_sections")` | Summarization| | [WordNet](https://wordnet.princeton.edu/) | (word, sentence, pos) | 27,123 | `load_dataset("rst", "wordnet_pos")` | Part-of-speech tagging| | [WordNet](https://wordnet.princeton.edu/) | (word, sentence, meaning, possible_meanings) | 27,123 | `load_dataset("rst", "wordnet_meaning")` | Word sense disambiguation| | [WordNet](https://wordnet.princeton.edu/) | (word, sentence, synonyms) | 17,804 | `load_dataset("rst", "wordnet_synonym")`| Paraphrasing| | [WordNet](https://wordnet.princeton.edu/) | (word, sentence, antonyms) | 6,408 | `load_dataset("rst", "wordnet_antonym")` |Negation | | [ConTRoL]() | (premise, hypothesis, label) | 8,323 | `load_dataset("rst", "qa_control")` | Natural language inference| |[DREAM](https://transacl.org/ojs/index.php/tacl/article/view/1534)| (context, question, options, answer) | 9,164 | `load_dataset("rst", "qa_dream")` | Reading comprehension| | [LogiQA](https://doi.org/10.24963/ijcai.2020/501) | (context, question, options, answer) | 7,974 | `load_dataset("rst", "qa_logiqa")` | Reading comprehension| | [ReClor](https://openreview.net/forum?id=HJgJtT4tvB) | (context, question, options, answer) | 5,138 | `load_dataset("rst", "qa_reclor")` |Reading comprehension | | [RACE](https://doi.org/10.18653/v1/d17-1082) | (context, question, options, answer) | 44,880 | `load_dataset("rst", "qa_race")` | Reading comprehension| | [RACE-C](http://proceedings.mlr.press/v101/liang19a.html) | (context, question, options, answer) | 5,093 | `load_dataset("rst", "qa_race_c")` | Reading comprehension| | [TriviaQA](https://doi.org/10.18653/v1/P17-1147) | (context, question, answer) | 46,636 | `load_dataset("rst", "qa_triviaqa")` |Reading comprehension | | [Arxiv](https://arxiv.org/) | (text, category) | 1,696,348 | `load_dataset("rst", "arxiv_category")` |Topic classification| | [Arxiv](https://arxiv.org/) | (text, summary) | 1,696,348 | `load_dataset("rst", "arxiv_summary")` | Summarization; Sentence expansion| | [Paperswithcode](https://paperswithcode.com/) | (text, entities, datasets, methods, tasks, metrics) | 4,731,233 | `load_dataset("rst", "paperswithcode_entity")` | Entity recognition| | [Paperswithcode](https://paperswithcode.com/) | (text, summary) | 120,924 | `load_dataset("rst", "paperswithcode_summary")` | Summarization; Sentence expansion| | e408abf7286020efc56bc8202769a775 |
afl-3.0 | [] | false | Bibtext for Citation Info ``` @article{yuan2022restructured, title={reStructured Pre-training}, author={Yuan, Weizhe and Liu, Pengfei}, journal={arXiv preprint arXiv:2206.11147}, year={2022} } ``` | 4e2cfc9f951b33b4e2c3df82cdb3eb0a |
apache-2.0 | ['generated_from_trainer'] | false | albert-large-v2_ner_wnut_17 This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.2429 - Precision: 0.7446 - Recall: 0.5335 - F1: 0.6216 - Accuracy: 0.9582 | 3e8c1ff5bd73bc55e8b53ce502e1504d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.3051 | 0.7929 | 0.3206 | 0.4566 | 0.9410 | | No log | 2.0 | 426 | 0.2151 | 0.7443 | 0.4665 | 0.5735 | 0.9516 | | 0.17 | 3.0 | 639 | 0.2310 | 0.7364 | 0.5012 | 0.5964 | 0.9559 | | 0.17 | 4.0 | 852 | 0.2387 | 0.7564 | 0.5311 | 0.6240 | 0.9578 | | 0.0587 | 5.0 | 1065 | 0.2429 | 0.7446 | 0.5335 | 0.6216 | 0.9582 | | b094b30adfb7262fce1497f63261aa59 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | whisper-tiny-hi This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7990 - Wer: 43.8869 | b45bb5d70dd04afaba0b0788d61a3804 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3000 - mixed_precision_training: Native AMP | 530ef642fdca80004a91fae0966f310f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1747 | 7.02 | 1000 | 0.5674 | 41.6800 | | 0.0466 | 14.03 | 2000 | 0.7042 | 43.7378 | | 0.0174 | 22.0 | 3000 | 0.7990 | 43.8869 | | f5eb50eb8c392f92486c60fff8f5eedb |
mit | [] | false | ํ์ต ํ๊ฒฝ ๋ฐ ํ์ดํผํ๋ผ๋ฏธํฐ - NVIDIA Tesla T4(16GB VRAM) - fp 16, deepspeed stage2 - 350000 steps, 2์ผ 17์๊ฐ ์์ - batch size 32 - learning rate 5e-5, linear scheduler - ์ต์ข
train loss: 3.684 - ํ์ต ์ฝ๋: https://github.com/HeegyuKim/language-model | d75884e17559f03e1eb2f0e2724f9406 |
mit | [] | false | <details> <summary>deepspeed parameter</summary> <div markdown="1"> ```json { "zero_optimization": { "stage": 2, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "allgather_partitions": true, "allgather_bucket_size": 5e8, "reduce_scatter": true, "reduce_bucket_size": 5e8, "overlap_comm": true, "contiguous_gradients": true }, "train_micro_batch_size_per_gpu": "auto", "train_batch_size": "auto", "steps_per_print": 1000 } ``` </div> </details> | eed5ddac1f52ced97f7fdfc0a6708de3 |
mit | [] | false | example ```python from transformers import pipeline generator = pipeline('text-generation', model='heegyu/kogpt-neox-tiny') def generate(prefix: str): print(generator(prefix, do_sample=True, top_p=0.6, repetition_penalty=1.4, max_length=128, penalty_alpha=0.6)[0]["generated_text"]) generate("0 : ๋ง์ฝ ์ค๋์ด ") generate("์ค๋ ์ ๋ถ๊ฐ ๋ฐํํ ๋ด์ฉ์ ๋ฐ๋ฅด๋ฉด") generate("์ํ์ด๋ ํ์๋ค์ ์ ์์ ๋ฐ๋ผ") generate("์์ ๋ณด๋๋ฐ ๋๋ฌด ์๊ฒจ ") ``` ์คํ ๊ฒฐ๊ณผ ``` 0 : ๋ง์ฝ ์ค๋์ด | 24abaa6ad9ddce572fe3db5128caf553 |
mit | [] | false | ๊ฐ ๋จผ์ ์๋๊ฑฐ๋ฉด ๋ ์๋๊ฑด๋ฐใ
1 : ใ
ใทใ_== 2 : ์๊น ์์นจ์ ์ผ์ด๋ฌ์ด?!! 3 : ์๋์๋ ๊ทผ๋ฐ ์ด๋ฐ ์๊ฐํ๊ฐ ๋๋ ๋๊น์ง ์ค์ง์๊ฒ ์ผ์ฃผ์ผ๋์ ๊ณ์ ์ ๋ค์์.. ๋๋ ์ง๊ธ ์ผ์ด๋ฌ๋๋ฐ, ๋๋ฌด ๋ฆ์๋ฏํด. ๊ทธ๋ฌ๋ค ๋ค์ ์ผ์ด๋์ ๋คํ์ด๋ค 4 : ์ด์ฐจํผ 2:30๋ถ์ ์ถ๋ฐํ ๊ฒ๊ฐ์์~ 5 : ์ด์ ๊ณง ์ผ์ด๋ซ์ด์ ์ค๋ ์ ๋ถ๊ฐ ๋ฐํํ ๋ด์ฉ์ ๋ฐ๋ฅด๋ฉด, ํ์ฐธ ์ฌ๋ถ๋ "ํ์จ์ด ์ด๋ฆด ์ ์๋ ๊ฒ ๋ฌด์์ธ๊ฐ"๋ผ๋ ์ง๋ฌธ์ ๋ํด ๋งํ ๊ฒ๋ ์๋ค. ํ์ง๋ง ๊ทธ๊ฑด ๋ฐ๋ก ์ด๋ฌํ ๋ฌธ์ ๋๋ฌธ์ผ ๊ฒ์ด๋ค." ์ค์ ๋ก ํด๋น ๊ธฐ์ฌ์์ ๋์จ ๋ฐ ์๋ค. ์ค์ ๋ก ๋น์ ํ๊ตญ์์ ์ด๊ฒ ์ฌ์ค์ด ์๋๋ผ๊ณ ๋ฐํ๋ค๋ ๊ฑด๋ฐ๋ ๋ถ๊ตฌํ๊ณ ๋ง์ด๋ค. ๊ธฐ์ฌํ๋๊ธฐ๋ ํ๋๋ฐ 'ํ๊ตญ์ด'์ ๊ฒฝ์ฐ์๋ ๋
ผ๋์ด ์์๋ค. ์ฌ์ค ์ด ๋ถ๋ถ๋ง ์ธ๊ธ๋์ด์๊ณ , ๋ํ๋ฏผ๊ตญ์ ๋ฌด์กฐ๊ฑด ๋น๋์ ํ๋ ๊ฒ์ด ์๋๋ผ ๋ณธ์ธ์ ์ค์๋ฅผ ์ ์ง๋ฅธ๋ค๋ ๊ฒ์ธ๋ฐ ๋ฐํด ์ ํ๋ธ ์ฑ๋์ ์์์์๋ ๊ทธ๋ฅ ์ ๋ฐ ๋๊ธ์ด ์ฌ๋ผ์ค ์ํ์ด๋ ํ์๋ค์ ์ ์์ ๋ฐ๋ผ ์ด ๊ต๊ณผ์์์ ๊ต์กํ๋ ๊ฒฝ์ฐ๊ฐ ๋ง์๋ฐ, ๊ทธ ์ด์ ๋ ๊ต์๋ค(์ค์ ๋ก ํ์๋ค์ ๊ณต๋ถ๋ ํ๊ตํ ์ ์๋ ๋ฑ)์ ํ๊ต๋ก ์ผ์ ๊ฐ์์ค์์ ๋ฃ๊ธฐ ๋๋ฌธ์ด๋ค. ์ด ํ๊ต์ ๊ต์ฌ๋ค์ด 'ํ๊ต'๋ฅผ ์ ํํ ๊ฒ์ ์๋์ง๋ง ๊ต์ฌ๊ฐ "ํ์๋ค์"๋ผ๋ ๋ป์ด๋ค."๋ผ๊ณ ํ๋ค. ํ์ง๋ง ์ด์ชฝ์ ๊ต์ฌ์ ํจ๊ป ํ ๋ช
์ฉ ์
ํ์ ์ ๋ถํฐ ๊ต์ฌ์ ์ธ์๋ค์ ์ํํด๋ณด๊ณ ์ถ๋ค๋ ์๋ฏธ๋ค. ๋ํ ์ํ์ฌํ์์๋ ๊ฐ๋ฅด์น ์๋ ์๊ณ ์ํ์ฌํ์ ๊ฐ๊ฑฐ๋ ์ ๊ณต ๊ณผ๋ชฉ์ผ๋ก ์กธ์
ํ๊ณ ๊ต์ฌ๋ ๋ค๋ฅธ ์์ ๋ณด๋๋ฐ ๋๋ฌด ์๊ฒจ | eeb899111d77883af49bd3efdaf84414 |
apache-2.0 | [] | false | **Don't use this model for any applied task. It too small to be practically useful. It is just a part of a weird research project.** An extremely small version of T5 with these parameters ```python "d_ff": 1024, "d_kv": 64, "d_model": 256, "num_heads": 4, "num_layers": 1, | e0abedb2923d05a5b3a7a27cb3e9ce82 |
apache-2.0 | [] | false | yes, just one layer ``` The model was pre-trained on `realnewslike` subset of C4 for 1 epoch with sequence length `64`. Corresponding WandB run: [click](https://wandb.ai/guitaricet/t5-lm/runs/2yvuxsfz?workspace=user-guitaricet). | 34f9d8bac61309621925089a4ede5c37 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_token_itr0_2e-05_all_16_02_2022-20_09_36 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1743 - Precision: 0.3429 - Recall: 0.3430 - F1: 0.3430 - Accuracy: 0.9446 | 43b8f631f3ee0a067418da0ebba8e39e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 38 | 0.3322 | 0.0703 | 0.1790 | 0.1010 | 0.8318 | | No log | 2.0 | 76 | 0.2644 | 0.1180 | 0.2343 | 0.1570 | 0.8909 | | No log | 3.0 | 114 | 0.2457 | 0.1624 | 0.2583 | 0.1994 | 0.8980 | | No log | 4.0 | 152 | 0.2487 | 0.1486 | 0.2583 | 0.1887 | 0.8931 | | No log | 5.0 | 190 | 0.2395 | 0.1670 | 0.2694 | 0.2062 | 0.8988 | | 35149f2da7bc79c06f296724b2c0c33e |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_unispeech-sat_s756 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (pt)](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. | 88e17c63c65c09bb497f7c1b79e213e7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the twitter-sentiment-analysis dataset. It achieves the following results on the evaluation set: - Loss: 0.4337 - Accuracy: 0.812 - Precision: 0.7910 - F1: 0.8042 | 07eaca11c2305ab10dd1f269650a3292 |
apache-2.0 | ['NER'] | false | Model description **mbert-base-uncased-ner-kin** is a model based on the fine-tuned Multilingual BERT base uncased model, previously fine-tuned for Named Entity Recognition using 10 high-resourced languages. It has been trained to recognize four types of entities: - dates & time (DATE) - Location (LOC) - Organizations (ORG) - Person (PER) | c12684cd5d3a0c3f73f94d082498f5de |
apache-2.0 | ['NER'] | false | Training Data This model was fine-tuned on the Kinyarwanda corpus **(kin)** of the [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset. However, we thresholded the number of entity groups per sentence in this dataset to 10 entity groups. | 7028efc794aca9561210ea5357faf3ca |
apache-2.0 | ['NER'] | false | Limitations - The size of the pre-trained language model prevents its usage in anything other than research. - Lack of analysis concerning the bias and fairness in these models may make them dangerous if deployed into production system. - The train data is a less populated version of the original dataset in terms of entity groups per sentence. Therefore, this can negatively impact the performance. | 343163b0a04b1b86d100a94742d9e1c9 |
apache-2.0 | ['NER'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("arnolfokam/mbert-base-uncased-ner-kin") model = AutoModelForTokenClassification.from_pretrained("arnolfokam/mbert-base-uncased-ner-kin") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Rayon Sports yasinyishije rutahizamu wโUmurundi" ner_results = nlp(example) print(ner_results) ``` | 22a4bb483acd47845ec35bb69cfe8172 |
cc-by-4.0 | ['ner'] | false | FastPDN FastPolDeepNer is model for Named Entity Recognition, designed for easy use, training and configuration. The forerunner of this project is [PolDeepNer2](https://gitlab.clarin-pl.eu/information-extraction/poldeepner2). The model implements a pipeline consisting of data processing and training using: hydra, pytorch, pytorch-lightning, transformers. Source code: https://gitlab.clarin-pl.eu/grupa-wieszcz/ner/fast-pdn | 2bafb72fa365869fa72da2a9539c3ea3 |
cc-by-4.0 | ['ner'] | false | How to use Here is how to use this model to get Named Entities in text: ```python from transformers import pipeline ner = pipeline('ner', model='clarin-pl/FastPDN', aggregation_strategy='simple') text = "Nazywam siฤ Jan Kowalski i mieszkam we Wrocลawiu." ner_results = ner(text) for output in ner_results: print(output) {'entity_group': 'nam_liv_person', 'score': 0.9996054, 'word': 'Jan Kowalski', 'start': 12, 'end': 24} {'entity_group': 'nam_loc_gpe_city', 'score': 0.998931, 'word': 'Wrocลawiu', 'start': 39, 'end': 48} ``` Here is how to use this model to get the logits for every token in text: ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("clarin-pl/FastPDN") model = AutoModelForTokenClassification.from_pretrained("clarin-pl/FastPDN") text = "Nazywam siฤ Jan Kowalski i mieszkam we Wrocลawiu." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | c6676d3d4c8f71cdc2a3b5f395119b25 |
cc-by-4.0 | ['ner'] | false | Training data The FastPDN model was trained on datasets (with 82 class versions) of kpwr and cen. Annotation guidelines are specified [here](https://clarin-pl.eu/dspace/bitstream/handle/11321/294/WytyczneKPWr-jednostkiidentyfikacyjne.pdf). | 28bc4284b9957557befe8025a4e7dcea |
cc-by-4.0 | ['ner'] | false | Pretraining FastPDN models have been fine-tuned, thanks to pretrained models: - [herbert-base-case](https://huggingface.co/allegro/herbert-base-cased) - [distiluse-base-multilingual-cased-v1](sentence-transformers/distiluse-base-multilingual-cased-v1) | a2359218f6bf61ccb75b5faf5298a8d9 |
cc-by-4.0 | ['ner'] | false | Evaluation Runs trained on `cen_n82` and `kpwr_n82`: | name |test/f1|test/pdn2_f1|test/acc|test/precision|test/recall| |---------|-------|------------|--------|--------------|-----------| |distiluse| 0.53 | 0.61 | 0.95 | 0.55 | 0.54 | | herbert | 0.68 | 0.78 | 0.97 | 0.7 | 0.69 | | 08ad756f16217bbd57b4068d22c05844 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-mrpc-target-glue-qqp This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-mrpc](https://huggingface.co/muhtasham/tiny-mlm-glue-mrpc) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4096 - Accuracy: 0.7995 - F1: 0.7718 | 32c178cae3f0352e8531c3e58439ffe4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 0.5796 | 0.04 | 500 | 0.5174 | 0.7297 | 0.6813 | | 0.5102 | 0.09 | 1000 | 0.4804 | 0.7541 | 0.7035 | | 0.4957 | 0.13 | 1500 | 0.4916 | 0.7412 | 0.7152 | | 0.4798 | 0.18 | 2000 | 0.4679 | 0.7549 | 0.7221 | | 0.4728 | 0.22 | 2500 | 0.4563 | 0.7624 | 0.7270 | | 0.4569 | 0.26 | 3000 | 0.4501 | 0.7673 | 0.7340 | | 0.4583 | 0.31 | 3500 | 0.4480 | 0.7682 | 0.7375 | | 0.4502 | 0.35 | 4000 | 0.4498 | 0.7665 | 0.7387 | | 0.4514 | 0.4 | 4500 | 0.4452 | 0.7681 | 0.7410 | | 0.4416 | 0.44 | 5000 | 0.4209 | 0.7884 | 0.7491 | | 0.4297 | 0.48 | 5500 | 0.4288 | 0.7826 | 0.7502 | | 0.4299 | 0.53 | 6000 | 0.4069 | 0.8001 | 0.7559 | | 0.4248 | 0.57 | 6500 | 0.4194 | 0.7896 | 0.7547 | | 0.4257 | 0.62 | 7000 | 0.4063 | 0.7998 | 0.7582 | | 0.418 | 0.66 | 7500 | 0.4059 | 0.8038 | 0.7639 | | 0.4306 | 0.7 | 8000 | 0.4111 | 0.7964 | 0.7615 | | 0.4212 | 0.75 | 8500 | 0.3990 | 0.8065 | 0.7672 | | 0.4143 | 0.79 | 9000 | 0.4227 | 0.7875 | 0.7604 | | 0.4121 | 0.84 | 9500 | 0.3906 | 0.8098 | 0.7667 | | 0.4138 | 0.88 | 10000 | 0.3872 | 0.8152 | 0.7725 | | 0.4082 | 0.92 | 10500 | 0.3843 | 0.8148 | 0.7700 | | 0.4084 | 0.97 | 11000 | 0.3863 | 0.8170 | 0.7740 | | 0.4067 | 1.01 | 11500 | 0.4001 | 0.8037 | 0.7707 | | 0.3854 | 1.06 | 12000 | 0.3814 | 0.8182 | 0.7756 | | 0.3945 | 1.1 | 12500 | 0.3861 | 0.8132 | 0.7761 | | 0.3831 | 1.14 | 13000 | 0.3917 | 0.8110 | 0.7750 | | 0.3722 | 1.19 | 13500 | 0.4096 | 0.7995 | 0.7718 | | 9365263a2d54e0d99b2a03efe2e12365 |
apache-2.0 | ['generated_from_trainer'] | false | Brain_Tumor_Classification_using_swin This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0123 - Accuracy: 0.9961 - F1: 0.9961 - Recall: 0.9961 - Precision: 0.9961 | a4ddcebfaacb72ee3b6820e93716328c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| | 0.1234 | 1.0 | 180 | 0.0450 | 0.9840 | 0.9840 | 0.9840 | 0.9840 | | 0.0837 | 2.0 | 360 | 0.0198 | 0.9926 | 0.9926 | 0.9926 | 0.9926 | | 0.0373 | 3.0 | 540 | 0.0123 | 0.9961 | 0.9961 | 0.9961 | 0.9961 | | f49cdcb28def39514d736e4a3bbecebf |
apache-2.0 | ['translation'] | false | opus-mt-ln-de * source languages: ln * target languages: de * OPUS readme: [ln-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ln-de/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/ln-de/opus-2020-01-21.zip) * test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ln-de/opus-2020-01-21.test.txt) * test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ln-de/opus-2020-01-21.eval.txt) | 28b7a40cc847d686425dbab38dee2aeb |
cc-by-4.0 | ['generated_from_trainer'] | false | CTEBMSP_ner_test This model is a fine-tuned version of [chizhikchi/Spanish_disease_finder](https://huggingface.co/chizhikchi/Spanish_disease_finder) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0560 - Diso Precision: 0.8925 - Diso Recall: 0.8945 - Diso F1: 0.8935 - Diso Number: 2645 - Overall Precision: 0.8925 - Overall Recall: 0.8945 - Overall F1: 0.8935 - Overall Accuracy: 0.9899 | 01c6804ffb6faae339dccffdfba9e874 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 714f32f3f90f2a8c4d631d2c507128fa |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.04 | 1.0 | 1570 | 0.0439 | 0.8410 | 0.8858 | 0.8628 | 2645 | 0.8410 | 0.8858 | 0.8628 | 0.9877 | | 0.0173 | 2.0 | 3140 | 0.0487 | 0.8728 | 0.8843 | 0.8785 | 2645 | 0.8728 | 0.8843 | 0.8785 | 0.9885 | | 0.0071 | 3.0 | 4710 | 0.0496 | 0.8911 | 0.8945 | 0.8928 | 2645 | 0.8911 | 0.8945 | 0.8928 | 0.9898 | | 0.0025 | 4.0 | 6280 | 0.0560 | 0.8925 | 0.8945 | 0.8935 | 2645 | 0.8925 | 0.8945 | 0.8935 | 0.9899 | | b442714dd9c0347a8a48e9d28bcf3230 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large V2 Spanish This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 es dataset. It achieves the following results on the evaluation set: - Loss: 0.1648 - Wer: 5.0745 | ea9e3e1af8fb15becdc933681d5ed4ae |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1500 | c53bdbffcc6ccfc9d23dc539f4c26c82 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1556 | 0.5 | 750 | 0.1683 | 5.0959 | | 0.1732 | 1.35 | 1500 | 0.1648 | 5.0745 | | abcdd620d0b405dc22e1a41a6f0dfb84 |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-pointer-top_v2 This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the top_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.0256 - Exact Match: 0.8517 | daa2cbc61de2c7354c4d30eff386a379 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 128 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 3000 | 644924120692b2ce4b7cb5681a3ea302 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | |:-------------:|:-----:|:----:|:---------------:|:-----------:| | 1.4545 | 0.82 | 200 | 0.2542 | 0.1294 | | 0.1878 | 1.65 | 400 | 0.0668 | 0.2128 | | 0.0796 | 2.47 | 600 | 0.0466 | 0.2276 | | 0.0536 | 3.29 | 800 | 0.0356 | 0.2309 | | 0.0424 | 4.12 | 1000 | 0.0317 | 0.2328 | | 0.0356 | 4.94 | 1200 | 0.0295 | 0.2340 | | 0.0306 | 5.76 | 1400 | 0.0288 | 0.2357 | | 0.0277 | 6.58 | 1600 | 0.0271 | 0.2351 | | 0.0243 | 7.41 | 1800 | 0.0272 | 0.2351 | | 0.0225 | 8.23 | 2000 | 0.0272 | 0.2353 | | 0.0206 | 9.05 | 2200 | 0.0267 | 0.2368 | | 0.0187 | 9.88 | 2400 | 0.0260 | 0.2367 | | 0.0173 | 10.7 | 2600 | 0.0256 | 0.2383 | | 0.0161 | 11.52 | 2800 | 0.0260 | 0.2383 | | 0.0153 | 12.35 | 3000 | 0.0257 | 0.2377 | | 8bc0e2f415db52b306eb0332910c119a |
apache-2.0 | ['speech'] | false | DistilXLSR-53 for BP [DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese](https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model. Paper: [DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT](https://arxiv.org/abs/2110.01900) Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee **Note 2**: The XLSR-53 model was distilled using [Brazilian Portuguese Datasets](https://huggingface.co/lgris/bp400-xlsr) for test purposes. The dataset is quite small to perform such task (the performance might not be so good as the [original work](https://arxiv.org/abs/2110.01900)). **Abstract** Self-supervised speech representation learning methods like wav2vec 2.0 and Hidden-unit BERT (HuBERT) leverage unlabeled speech data for pre-training and offer good representations for numerous speech processing tasks. Despite the success of these methods, they require large memory and high pre-training costs, making them inaccessible for researchers in academia and small companies. Therefore, this paper introduces DistilHuBERT, a novel multi-task learning framework to distill hidden representations from a HuBERT model directly. This method reduces HuBERT's size by 75% and 73% faster while retaining most performance in ten different tasks. Moreover, DistilHuBERT required little training time and data, opening the possibilities of pre-training personal and on-device SSL models for speech. | 707e4d2bfdd84b86c94ee935c3d18c4c |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/domain_transfer_clinic_credit_cards-massive_iot-roberta-large-v1-2-6 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. | ac1c556f9b822c84b1d485f52b4ed846 |
mit | ['generated_from_trainer'] | false | bert-base-historic-multilingual-cased-squad-fr This model is a fine-tuned version of [dbmdz/bert-base-historic-multilingual-cased](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7001 | 36b87462db3c19b8557b23368c2e5317 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9769 | 1.0 | 3660 | 1.8046 | | 1.6309 | 2.0 | 7320 | 1.7001 | | ed7d1c24c778bd6b6ac94394f0d00457 |
apache-2.0 | ['generated_from_trainer'] | false | barthez-deft-linguistique This model is a fine-tuned version of [moussaKam/barthez](https://huggingface.co/moussaKam/barthez) on an unknown dataset. **Note**: this model is one of the preliminary experiments and it underperforms the models published in the paper (using [MBartHez](https://huggingface.co/moussaKam/mbarthez) and HAL/Wiki pre-training + copy mechanisms) It achieves the following results on the evaluation set: - Loss: 1.7596 - Rouge1: 41.989 - Rouge2: 22.4524 - Rougel: 32.7966 - Rougelsum: 32.7953 - Gen Len: 22.1549 | 300e66dd0996a00d93556d3886e64756 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-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: 20.0 - mixed_precision_training: Native AMP | 7fc310197688b1d4a577e24eda1f2722 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 3.0569 | 1.0 | 108 | 2.0282 | 31.6993 | 14.9483 | 25.5565 | 25.4379 | 18.3803 | | 2.2892 | 2.0 | 216 | 1.8553 | 35.2563 | 18.019 | 28.3135 | 28.2927 | 18.507 | | 1.9062 | 3.0 | 324 | 1.7696 | 37.4613 | 18.1488 | 28.9959 | 29.0134 | 19.5352 | | 1.716 | 4.0 | 432 | 1.7641 | 37.6903 | 18.7496 | 30.1097 | 30.1027 | 18.9577 | | 1.5722 | 5.0 | 540 | 1.7781 | 38.1013 | 19.8291 | 29.8142 | 29.802 | 19.169 | | 1.4655 | 6.0 | 648 | 1.7661 | 38.3557 | 20.3309 | 30.5068 | 30.4728 | 19.3662 | | 1.3507 | 7.0 | 756 | 1.7596 | 39.7409 | 20.2998 | 31.0849 | 31.1152 | 19.3944 | | 1.2874 | 8.0 | 864 | 1.7706 | 37.7846 | 20.3457 | 30.6826 | 30.6321 | 19.4789 | | 1.2641 | 9.0 | 972 | 1.7848 | 38.7421 | 19.5701 | 30.5798 | 30.6305 | 19.3944 | | 1.1192 | 10.0 | 1080 | 1.8008 | 40.3313 | 20.3378 | 31.8325 | 31.8648 | 19.5493 | | 1.0724 | 11.0 | 1188 | 1.8450 | 38.9612 | 20.5719 | 31.4496 | 31.3144 | 19.8592 | | 1.0077 | 12.0 | 1296 | 1.8364 | 36.5997 | 18.46 | 29.1808 | 29.1705 | 19.7324 | | 0.9362 | 13.0 | 1404 | 1.8677 | 38.0371 | 19.2321 | 30.3893 | 30.3926 | 19.6338 | | 0.8868 | 14.0 | 1512 | 1.9154 | 36.4737 | 18.5314 | 29.325 | 29.3634 | 19.6479 | | 0.8335 | 15.0 | 1620 | 1.9344 | 35.7583 | 18.0687 | 27.9666 | 27.8675 | 19.8028 | | 0.8305 | 16.0 | 1728 | 1.9556 | 37.2137 | 18.2199 | 29.5959 | 29.5799 | 19.9577 | | 0.8057 | 17.0 | 1836 | 1.9793 | 36.6834 | 17.8505 | 28.6701 | 28.7145 | 19.7324 | | 0.7869 | 18.0 | 1944 | 1.9994 | 37.5918 | 19.1984 | 28.8569 | 28.8278 | 19.7606 | | 0.7549 | 19.0 | 2052 | 2.0117 | 37.3278 | 18.5169 | 28.778 | 28.7737 | 19.8028 | | 0.7497 | 20.0 | 2160 | 2.0189 | 37.7513 | 19.1813 | 29.3675 | 29.402 | 19.6901 | | 5b7828eaca00a60c797f21f59d7e863d |
apache-2.0 | ['generated_from_trainer', 'translation'] | false | mt-hr-sv-finetuned This model is a fine-tuned version of [Helsinki-NLP/opus-mt-hr-sv](https://huggingface.co/Helsinki-NLP/opus-mt-hr-sv) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.9565 - eval_bleu: 49.8248 - eval_runtime: 873.8605 - eval_samples_per_second: 16.982 - eval_steps_per_second: 4.246 - epoch: 5.0 - step: 27825 | a625b181e0717e7bb7810dcb0aeaa724 |
apache-2.0 | ['generated_from_trainer', 'translation'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 24 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP | ef0a16d0d7eb59be5eccec46fd513eac |
apache-2.0 | ['text-to-speech', 'TTS', 'speech-synthesis', 'Tacotron2', 'speechbrain'] | false | Text-to-Speech (TTS) with Tacotron2 trained on LJSpeech This repository provides all the necessary tools for Text-to-Speech (TTS) with SpeechBrain using a [Tacotron2](https://arxiv.org/abs/1712.05884) pretrained on [LJSpeech](https://keithito.com/LJ-Speech-Dataset/). The pre-trained model takes in input a short text and produces a spectrogram in output. One can get the final waveform by applying a vocoder (e.g., HiFIGAN) on top of the generated spectrogram. | f80c41243a037de0806cb7971e8a169b |
apache-2.0 | ['text-to-speech', 'TTS', 'speech-synthesis', 'Tacotron2', 'speechbrain'] | false | Intialize TTS (tacotron2) and Vocoder (HiFIGAN) tacotron2 = Tacotron2.from_hparams(source="speechbrain/tts-tacotron2-ljspeech", savedir="tmpdir_tts") hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="tmpdir_vocoder") | 1b54e240aa5a7863bea4c3d28bdafd7c |
apache-2.0 | ['text-to-speech', 'TTS', 'speech-synthesis', 'Tacotron2', 'speechbrain'] | false | Save the waverform torchaudio.save('example_TTS.wav',waveforms.squeeze(1), 22050) ``` If you want to generate multiple sentences in one-shot, you can do in this way: ``` from speechbrain.pretrained import Tacotron2 tacotron2 = Tacotron2.from_hparams(source="speechbrain/TTS_Tacotron2", savedir="tmpdir") items = [ "A quick brown fox jumped over the lazy dog", "How much wood would a woodchuck chuck?", "Never odd or even" ] mel_outputs, mel_lengths, alignments = tacotron2.encode_batch(items) ``` | ca9e72be7d0c5f671b35a71414bf19de |
apache-2.0 | ['text-to-speech', 'TTS', 'speech-synthesis', 'Tacotron2', 'speechbrain'] | false | Training The model was trained with SpeechBrain. To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/LJSpeech/TTS/tacotron2/ python train.py --device=cuda:0 --max_grad_norm=1.0 --data_folder=/your_folder/LJSpeech-1.1 hparams/train.yaml ``` You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1PKju-_Nal3DQqd-n0PsaHK-bVIOlbf26?usp=sharing). | 064b02c933e9db7cc766203275dd62d7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-imdb-demo This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.4328 - Accuracy: 0.928 | 15e9886f9e8354bb4e94b59e93282e87 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 | d353b7be4516f3ab1250e04ec6a59c24 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3459 | 1.0 | 2657 | 0.2362 | 0.9091 | | 0.1612 | 2.0 | 5314 | 0.2668 | 0.9248 | | 0.0186 | 3.0 | 7971 | 0.3274 | 0.9323 | | 0.1005 | 4.0 | 10628 | 0.3978 | 0.9277 | | 0.0006 | 5.0 | 13285 | 0.4328 | 0.928 | | 35b767e3768187f6eec7674d11ac4d55 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | VIBES-2,-Checkpoint-1 Dreambooth model trained by darkvibes 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) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: | 9743060c4e7538cafcf97fc935a8a5b2 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xlsr-53-espeak-cv-ft-xas3-ntsema-colab This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 4.3037 - Wer: 0.9713 | 1455ef361d6e3313772b73c9620d420b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP | 8a1fa1c5adc17e951f4e1d5fbd3c5bb0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 7.2912 | 9.09 | 400 | 3.9091 | 1.0 | | 2.5952 | 18.18 | 800 | 3.8703 | 0.9959 | | 2.3509 | 27.27 | 1200 | 4.3037 | 0.9713 | | 636c062e4dd1c8a06dd4bd33a393ba56 |
apache-2.0 | ['translation'] | false | bul-ita * source group: Bulgarian * target group: Italian * OPUS readme: [bul-ita](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-ita/README.md) * model: transformer * source language(s): bul * target language(s): ita * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ita/opus-2020-07-03.zip) * test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ita/opus-2020-07-03.test.txt) * test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ita/opus-2020-07-03.eval.txt) | 33e9764d51560a776a234061b564123e |
apache-2.0 | ['translation'] | false | System Info: - hf_name: bul-ita - source_languages: bul - target_languages: ita - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-ita/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['bg', 'it'] - src_constituents: {'bul', 'bul_Latn'} - tgt_constituents: {'ita'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ita/opus-2020-07-03.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ita/opus-2020-07-03.test.txt - src_alpha3: bul - tgt_alpha3: ita - short_pair: bg-it - chrF2_score: 0.653 - bleu: 43.1 - brevity_penalty: 0.987 - ref_len: 16951.0 - src_name: Bulgarian - tgt_name: Italian - train_date: 2020-07-03 - src_alpha2: bg - tgt_alpha2: it - prefer_old: False - long_pair: bul-ita - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 816c199b44fff29408f8d01563b620eb |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UR dataset. It achieves the following results on the evaluation set: - Loss: 3.8433 - Wer: 0.9852 | a170f6dbb998cacf432af9f4c7c266bc |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2000 - mixed_precision_training: Native AMP | 77480b423065e4d1e069bb0839e9ee92 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 1.468 | 166.67 | 500 | 3.0262 | 1.0035 | | 0.0572 | 333.33 | 1000 | 3.5352 | 0.9721 | | 0.0209 | 500.0 | 1500 | 3.7266 | 0.9834 | | 0.0092 | 666.67 | 2000 | 3.8433 | 0.9852 | | c4a576d3c8090d6881869e904d65f7df |
mit | ['exbert'] | false | Overview **Language model:** gelectra-base-germanquad-distilled **Language:** German **Training data:** GermanQuAD train set (~ 12MB) **Eval data:** GermanQuAD test set (~ 5MB) **Infrastructure**: 1x V100 GPU **Published**: Apr 21st, 2021 | ef2787a61f8db9a9164ae140e8d84a3d |
mit | ['exbert'] | false | Details - We trained a German question answering model with a gelectra-base model as its basis. - The dataset is GermanQuAD, a new, German language dataset, which we hand-annotated and published [online](https://deepset.ai/germanquad). - The training dataset is one-way annotated and contains 11518 questions and 11518 answers, while the test dataset is three-way annotated so that there are 2204 questions and with 2204ยท3โ76 = 6536answers, because we removed 76 wrong answers. - In addition to the annotations in GermanQuAD, haystack's distillation feature was used for training. deepset/gelectra-large-germanquad was used as the teacher model. See https://deepset.ai/germanquad for more details and dataset download in SQuAD format. | 3aa6f8b377f182cd090bab18a6b956e0 |
mit | ['exbert'] | false | Performance We evaluated the extractive question answering performance on our GermanQuAD test set. Model types and training data are included in the model name. For finetuning XLM-Roberta, we use the English SQuAD v2.0 dataset. The GELECTRA models are warm started on the German translation of SQuAD v1.1 and finetuned on \\\\germanquad. The human baseline was computed for the 3-way test set by taking one answer as prediction and the other two as ground truth. ``` "exact": 62.4773139745916 "f1": 80.9488017070188 ```  | faa5650cdcd2b6f87b42b82fbb2c773f |
mit | ['exbert'] | false | Authors - Timo Mรถller: `timo.moeller [at] deepset.ai` - Julian Risch: `julian.risch [at] deepset.ai` - Malte Pietsch: `malte.pietsch [at] deepset.ai` - Michel Bartels: `michel.bartels [at] deepset.ai` | 52c5a2692eaba2de33b738c76931a31f |
mit | ['exbert'] | false | About us  We bring NLP to the industry via open source! Our focus: Industry specific language models & large scale QA systems. Some of our work: - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) - [FARM](https://github.com/deepset-ai/FARM) - [Haystack](https://github.com/deepset-ai/haystack/) Get in touch: [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs) | 3eeb41bdfdb3d31db88fad65fc96c8dd |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 5 | 5e4725885dbf1f5ee12f0f22ff6a1d09 |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_xls-r_age_teens-10_sixties-0_s380 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 (de)](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. | ea8ff687fdc17b67c86b1c812021ad91 |
apache-2.0 | ['science', 'multi-displinary'] | false | ScholarBERT_100_WB Model This is the **ScholarBERT_100_WB** variant of the ScholarBERT model family. The model is pretrained on a large collection of scientific research articles (**221B tokens**). Additionally, the pretraining data also includes the Wikipedia+BookCorpus, which are used to pretrain the [BERT-base](https://huggingface.co/bert-base-cased) and [BERT-large](https://huggingface.co/bert-large-cased) models. This is a **cased** (case-sensitive) model. The tokenizer will not convert all inputs to lower-case by default. The model is based on the same architecture as [BERT-large](https://huggingface.co/bert-large-cased) and has a total of 340M parameters. | c9eae7987b128624c60ba1a5b71234a9 |
apache-2.0 | ['science', 'multi-displinary'] | false | Training Dataset The vocab and the model are pertrained on **100% of the PRD** scientific literature dataset and the Wikipedia+BookCorpus. The PRD dataset is provided by Public.Resource.Org, Inc. (โPublic Resourceโ), a nonprofit organization based in California. This dataset was constructed from a corpus of journal article files, from which We successfully extracted text from 75,496,055 articles from 178,928 journals. The articles span across Arts & Humanities, Life Sciences & Biomedicine, Physical Sciences, Social Sciences, and Technology. The distribution of articles is shown below.  | 7ae21ff9e39d2b436b4c75073e62f31e |
apache-2.0 | ['science', 'multi-displinary'] | false | BibTeX entry and citation info If using this model, please cite this paper: ``` @misc{hong2022scholarbert, doi = {10.48550/ARXIV.2205.11342}, url = {https://arxiv.org/abs/2205.11342}, author = {Hong, Zhi and Ajith, Aswathy and Pauloski, Gregory and Duede, Eamon and Malamud, Carl and Magoulas, Roger and Chard, Kyle and Foster, Ian}, title = {ScholarBERT: Bigger is Not Always Better}, publisher = {arXiv}, year = {2022} } ``` | 0943b932a8c51828e721e9972b2f3f7f |
apache-2.0 | ['translation'] | false | opus-mt-ja-en * source languages: ja * target languages: en * OPUS readme: [ja-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ja-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/ja-en/opus-2019-12-18.zip) * test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ja-en/opus-2019-12-18.test.txt) * test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ja-en/opus-2019-12-18.eval.txt) | f26b96606b795459c0b8f2cb50163b4a |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/msmarco-distilbert-base-tas-b This is a port of the [DistilBert TAS-B Model](https://huggingface.co/sebastian-hofstaetter/distilbert-dot-tas_b-b256-msmarco) to [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is optimized for the task of semantic search. | 69ccc73b8b4afa654b342c99fe8d3500 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer, util query = "How many people live in London?" docs = ["Around 9 Million people live in London", "London is known for its financial district"] | ff4e1701b9f36fc68333a26dc8dda9b8 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-distilbert-base-tas-b") model = AutoModel.from_pretrained("sentence-transformers/msmarco-distilbert-base-tas-b") | 0b1394c5e899f83346be688c95b5f27e |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/msmarco-distilbert-base-tas-b) | 8701f678ae2556c8197aa0b2f8bfa538 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` | 5daf2d76bedbf0818ca86e9511f8711a |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ft1500_norm1000 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0875 - Mse: 1.3594 - Mae: 0.5794 - R2: 0.3573 - Accuracy: 0.7015 | 2fb20aa75c3ebc496700f5bb47b0744a |
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: 5 | 72d24a9f042d89d26a053e58117792b4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:--------:| | 0.8897 | 1.0 | 3122 | 1.0463 | 1.3078 | 0.5936 | 0.3817 | 0.7008 | | 0.7312 | 2.0 | 6244 | 1.0870 | 1.3588 | 0.5796 | 0.3576 | 0.7002 | | 0.5348 | 3.0 | 9366 | 1.1056 | 1.3820 | 0.5786 | 0.3467 | 0.7124 | | 0.3693 | 4.0 | 12488 | 1.0866 | 1.3582 | 0.5854 | 0.3579 | 0.7053 | | 0.2848 | 5.0 | 15610 | 1.0875 | 1.3594 | 0.5794 | 0.3573 | 0.7015 | | a61f44f6d74de260d1bed793199edbf7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 0.0083 | 6cc249faa08e9ee4c16dc4bf7961cbf3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2258 | 1.0 | 5533 | 0.0560 | | 0.952 | 2.0 | 11066 | 0.0096 | | 0.7492 | 3.0 | 16599 | 0.0083 | | f48e2527effc9b272e3756586c6ceb7e |
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