license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['translation'] | false | tur-ukr * source group: Turkish * target group: Ukrainian * OPUS readme: [tur-ukr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tur-ukr/README.md) * model: transformer-align * source language(s): tur * target language(s): ukr * model: transformer-align * pre-processing: normalization + Se... | 13f08a56320ebed29499b5e922ec075a |
apache-2.0 | ['translation'] | false | System Info: - hf_name: tur-ukr - source_languages: tur - target_languages: ukr - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tur-ukr/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['tr', 'uk'] - src_constituents: {'tur'} - tgt_const... | 52b7254db62b2ad1f7a3605207b1c7fb |
cc-by-4.0 | ['questions and answers generation'] | false | Model Card of `lmqg/bart-large-tweetqa-qag` This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question & answer pair generation task on the [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) (dataset_name: default) via [`lmqg`](https://github.co... | 18b1fa9a7adcc5e36fa1e54803dfead8 |
cc-by-4.0 | ['questions and answers generation'] | false | Overview - **Language model:** [facebook/bart-large](https://huggingface.co/facebook/bart-large) - **Language:** en - **Training data:** [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.co... | 35580d170d548cf9d5254b613913d9b9 |
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/bart-large-tweetqa-qag") output = pipe("Beyonce further ex... | e797b3e863d45e41fb04127031a768db |
cc-by-4.0 | ['questions and answers generation'] | false | Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-large-tweetqa-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_tweetqa.default.json) | | Score | Type | Dataset ... | 9d870d1e8b9dd8bc4563a4fda6c3ba0e |
cc-by-4.0 | ['questions and answers generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_tweetqa - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: facebook/bart-large - max_length: 256 - max_length_output: 128 - epoch... | c674505a44dda10dc09cfab2b5b8ec60 |
apache-2.0 | ['generated_from_trainer'] | false | Sentiment140_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the sentiment140 dataset. It achieves the following results on the evaluation set: - Loss: 0.6103 - Accuracy: 0.8533 | 19294a82439a37bacd66a3ed9de6ef82 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6713 | 0.08 | 50 | 0.5704 | 0.7333 | | 0.5742 | 0.16 | 100 | 0.4620 | 0.8 | | 0.5104 | 0.24 | 150 | 0.5536 | 0.... | f44d0b89ccee2ea3a91b22bf72e67e6a |
cc-by-4.0 | [] | false | Usage ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sarnikowski/electra-small-discriminator-da-256-cased") model = AutoModel.from_pretrained("sarnikowski/electra-small-discriminator-da-256-cased") ``` | cb5090b6a90ac936d7c1d2c41305de38 |
cc-by-4.0 | [] | false | Questions? If you have any questions feel free to open an issue on the [danish_transformers](https://github.com/sarnikowski/danish_transformers) repository, or send an email to p.sarnikowski@gmail.com | 494a2a94ba6d07d842c31ad02a963917 |
apache-2.0 | ['translation'] | false | opus-mt-gil-fr * source languages: gil * target languages: fr * OPUS readme: [gil-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/gil-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | e1f662e5d01d678a6b5721513d95671d |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-entailement-Writer-T5-small This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5628 | ccdcaefa193a6bd061647fe6c4ddd51e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 83 | 1.2943 | | No log | 2.0 | 166 | 0.9323 | | No log | 3.0 | 249 | 0.8443 | | No log | 4.0 | 332 | 0.7884 ... | 3ab786bbe90c5d7d2766289984c2e6bb |
creativeml-openrail-m | [] | false | Use 'wewulzkz' as the keyword. A bit overcooked but gets the job done, I wanted a model that could create a good base for werewolves that I could then paint over, and this serves that purpose well. Based on SD 1.5.  It is also pretty good at... | 045f36734b34de76e2d6a5d9628dd98d |
mit | ['summarization'] | false | Pre-trained BART Model fine-tune on WikiLingua dataset The repository for the fine-tuned BART model (by sshleifer) using the **wiki_lingua** dataset (English) **Purpose:** Examine the performance of a fine-tuned model research purposes **Observation:** - Pre-trained model was trained on the XSum dataset, which summa... | edf63d520d044708b74a9136ded75f8d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 24 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | bd7c94e2b0acc80ba41428b481456ae4 |
apache-2.0 | ['translation'] | false | opus-mt-fi-ts * source languages: fi * target languages: ts * OPUS readme: [fi-ts](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-ts/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://... | 9d63c9dacb513b75e4ab58739edb2ef8 |
cc-by-4.0 | ['answer extraction'] | false | Model Card of `lmqg/mt5-base-koquad-ae` This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for answer extraction on the [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ... | 759bc5bf28a58f2b9da5245cd2d27a02 |
cc-by-4.0 | ['answer extraction'] | false | model prediction answers = model.generate_a("1990๋
์ํ ใ ๋จ๋ถ๊ตฐ ใ์์ ๋จ์ญ์ผ๋ก ์ํ๋ฐฐ์ฐ ์ฒซ ๋ฐ๋ท์ ์ด์ด ๊ฐ์ ํด KBS ๋๋ผ๋ง ใ์ง๊ตฌ์ธใ์์ ๋จ์ญ์ผ๋ก ์ถ์ฐํ์๊ณ ์ด๋ฌํด MBC ใ์ฌ๋ช
์ ๋๋์ใ๋ฅผ ํตํด ๋จ์ญ์ผ๋ก ์ถ์ฐํ์๋ค.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-base-koquad-ae") output = pipe("๋ํ ์คํผ์ด์ค๋ ๋ง์ ์... | 21506eb71cf9cef907fa50e670ae5fbf |
cc-by-4.0 | ['answer extraction'] | false | Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-koquad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_koquad.default.json) | | Score | Type | Dataset | |:----... | b074cddb3783e49adb8d26e6fbe50bc9 |
cc-by-4.0 | ['answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_koquad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: None - model: google/mt5-base - max_length: 512 - max_length_output: 32 - epoch: 5 - ba... | 39b193c76eb117719e20ee0559a4b6a3 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-LARGE-KV128 (Deep-Narrow version) T5-Efficient-LARGE-KV128 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoin... | 7f8d1ad91be9f189d2ec884d5e2cb8ce |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-large-kv128** - is of model type **Large** with the following variations: - **kv** is **128** It has **1039.71** million parameters and thus requires *ca.* **4158.86 MB** of memory in full precision (*fp32*) or **2079.43 MB** of memory in half preci... | 1c1dd5262e731ab918ac3bcbf5168cfe |
mit | ['conversational'] | false | personachat-arabic (conversational AI) This is personachat-arabic, using a subset from the persona-chat validation dataset, machine translated to Arabic (from English) and fine-tuned from [akhooli/gpt2-small-arabic](https://huggingface.co/akhooli/gpt2-small-arabic) which is a limited text generation model. Usage: s... | 355a4d63a87db4472f2bd1d89d8671de |
apache-2.0 | ['generated_from_trainer'] | false | insertion-prop05-ls01 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2120 - Precision: 0.9800 - Recall: 0.9776 - F1: 0.9788 - Accuracy: 0.9924 | fa069a87439443bba792e18db6f0b00e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - label_smoothing_factor: 0.1 | 5c055301ae95d3be80c68db0033865e0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2462 | 0.32 | 500 | 0.2160 | 0.9754 | 0.9697 | 0.9725 | 0.9902 | | 0.2194 | 0.64 |... | 2635c79814db5dc51e4bd44d66114a26 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-NL24 (Deep-Narrow version) T5-Efficient-BASE-NL24 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint an... | bba1ba0e232b0c42d458505aeb2a013f |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-nl24** - is of model type **Base** with the following variations: - **nl** is **24** It has **421.19** million parameters and thus requires *ca.* **1684.75 MB** of memory in full precision (*fp32*) or **842.37 MB** of memory in half precision (... | ba505c7ce97259864199ec4c12932980 |
cc-by-4.0 | ['hi', 'en', 'codemix'] | false | HingRoBERTa-Mixed HingRoBERTa-Mixed is a Hindi-English code-mixed BERT model trained on roman + devanagari text. It is a xlm-RoBERTa model fine-tuned on mixed script L3Cube-HingCorpus. <br> [dataset link] (https://github.com/l3cube-pune/code-mixed-nlp) More details on the dataset, models, and baseline results can be ... | 6acab6cf9cd88d9ca338258bc25957b7 |
apache-2.0 | ['translation'] | false | opus-mt-fr-srn * source languages: fr * target languages: srn * OPUS readme: [fr-srn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-srn/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | bf8e195d3f10482559986697d226a33f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Vi v1 - Shiv Kumar Ganesh This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 1.0641 - Wer: 34.0974 | e313cbe619ffde57d0f2148edff17f3e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0005 | 31.0 | 500 | 0.7179 | 33.7464 | | 0.0002 | 62.0 | 1000 | 0.7837 | 32.4742 | | 0.0001 | 93.0 | 1500 | 0.8267 | 34.272... | d3d7098c4bcdf6ceaafcb74fe33e9109 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0008 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 4c8f13f32fd0014f5659cbb3321a7c88 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, ... | 84db4350c2c7930d866da99713e90eba |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.6380 - F1: 0.5542 | e80f4f48c6e33850c2ca4ed6b93de741 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 13 | 1.0388 | 0.1801 | | No log | 2.0 | 26 | 0.7545 | 0.5053 | | No log | 3.0 | 39 | 0.6380 | 0.5542 | ... | f870f161a9d27bd08ce8c7af7a52337e |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.3892 - F1: 0.6859 | 4648e6bb0f6df532ecd9b76de4686033 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1135 | 1.0 | 50 | 0.5347 | 0.5463 | | 0.4935 | 2.0 | 100 | 0.4424 | 0.6338 | | 0.3732 | 3.0 | 150 | 0.3892 | 0.6859 | ... | 2b131da1864eaf7fbca6392b7dbec384 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-German Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on German using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. | e1d7bd7ae1fab90f4546da07da6ba628 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "de", split="test[:2%]") processor = Wav2Vec2Processor.from_... | 4c0d7fbd20f4a7764d87dd39f5a32739 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the {language} test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "de", split="test") ... | db1417632630bef467230b0b3616981a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch... | 4986e8c944e48228a5b25b4b872423fd |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() for x in substitutions: batch["sentence"] = re.sub(substitutions[x], x, batch["sentence"]) speech_array, sampling_rate = torchaudio.load... | 06502ad49c22fdd9cb90dea3afe3d493 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch... | 17f552d3c0a21f21c1d0915cd58c5e18 |
apache-2.0 | ['generated_from_keras_callback'] | false | bert-finetuned-ner-per-v7 This model is a fine-tuned version of [BeardedJohn/bert-finetuned-ner-ubb-conll-endava-only-misc-v2](https://huggingface.co/BeardedJohn/bert-finetuned-ner-ubb-conll-endava-only-misc-v2) on an unknown dataset. It achieves the following results on the evaluation set: | 2331e0ee6c5e4ece97fffbc7fd8cf0a5 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 313, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': ... | 3a4ccaf696d0ba6e367529b41ce67d3e |
apache-2.0 | ['mobile', 'vison', 'image-classification'] | false | Model Details <!-- Give an overview of your model, the relevant research paper, who trained it, etc. --> EfficientFormer-L3, developed by [Snap Research](https://github.com/snap-research), is one of three EfficientFormer models. The EfficientFormer models were released as part of an effort to prove that properly d... | 188d23c753f854712a17c90160635430 |
apache-2.0 | ['generated_from_trainer'] | false | t5-large-finetune-keyword-to-text-generation This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1471 - Rouge1: 2.175 - Rouge2: 0.3661 - Rougel: 1.7927 - Rougelsum: 1.7951 - Gen Len: 15.3252 | 4d0a905c5ba04aff73bef44983a20b24 |
apache-2.0 | ['generated_from_trainer'] | false | Model description This model is designed to generate text from a single keyword. This project is intended to be used for generating vocabulary questions for ed-tech applications. NOTE!: Be sure to use the 'summarize: ' prefix before the word that you would like to un-summarize. | 2e260999e88eee3d755241d90b7abc2e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 3.3083 | 1.0 | 3000 | 3.1706 | 2.1498 | 0.331 | 1.7579 | 1.761 | 16.6826 ... | 0f71377a9d3f53d63c875bcdb7d42129 |
apache-2.0 | ['generated_from_keras_callback'] | false | DamianCummins/distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0556 - Validation Loss: 0.0608 - Train Precision: 0.9196 -... | f9a71230bcbe19ce15830144a0ef8fcb |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.0556 | 0.0608 | 0.9196 | 0.9304 | 0.9250 | 0.9820 | 0 ... | 4e1fde11fe3e0abcd0fab01095d13b42 |
apache-2.0 | ['generated_from_trainer'] | false | M6_cross 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: 0.0084 - Pearson: 0.9811 - Spearmanr: 0.9075 | 1fd26518dfa35334a87d747f4fd79791 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 20 - eval_batch_size: 20 - seed: 25 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 6.0 - num_epochs: 5 - mixed_precision_tra... | 1b3d344de76796529b2b6a97f2594a13 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | 0.0059 | 1.0 | 105 | 0.0158 | 0.9633 | 0.9054 | | 0.001 | 2.0 | 210 | 0.0102 | 0.9770 | 0.9103 | | 0.0008 ... | 83bdf850dad73cb49811332ddd716a7f |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `Shinji_Watanabe/laborotv_asr_train_asr_conformer2_latest33_raw_char_sp_valid.acc.ave` โป๏ธ Imported from https://zenodo.org/record/4304245/ This model was trained by Shinji Watanabe using laborotv/asr1 recipe in [espnet](https://github.com/espnet/espnet/). | 72ec830019d037a92f4329874acce30b |
apache-2.0 | ['thai', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [roberta-base-thai-syllable-upos](https://huggingface.co/KoichiYasuoka/roberta-base-thai-syllable-upos). | 9083688c8a785b31c2442188b495c07a |
apache-2.0 | ['thai', 'token-classification', 'pos', 'dependency-parsing'] | false | text = "+text+"\n" v=[(s,e) for s,e in w["offset_mapping"] if s<e] for i,(s,e) in enumerate(v,1): q=self.model.config.id2label[p[i,h[i]]].split("|") u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n" return... | 4ed21be46d89a7e40b7254645cfab143 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2238 - Accuracy: 0.922 - F1: 0.9221 | 9c61ea197f9a9720c1cc1ce743ad7425 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.829 | 1.0 | 250 | 0.3173 | 0.9005 | 0.8980 | | 0.247 | 2.0 | 500 | 0.2238 | 0.922 | 0.9221 | | 36012314e2698f4243a1a1f17ae417ad |
apache-2.0 | ['generated_from_keras_callback'] | false | amitjohn007/bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5685 - Epoch: 2 | c6ab106f6cf2ce19c0dcc6f2db4d70b6 |
apache-2.0 | ['generated_from_keras_callback'] | false | long-t5-local-base This model is a fine-tuned version of [google/long-t5-local-base](https://huggingface.co/google/long-t5-local-base) on an unknown dataset. It achieves the following results on the evaluation set: | 75b574f1278b48ec96c43e0aa75d1008 |
apache-2.0 | ['audio', 'TTS'] | false | load the model and tokenizer from fastspeech2_hf.modeling_fastspeech2 import FastSpeech2ForPretraining, FastSpeech2Tokenizer model = FastSpeech2ForPretraining.from_pretrained("ontocord/fastspeech2-en") tokenizer = FastSpeech2Tokenizer.from_pretrained("ontocord/fastspeech2-en") | e5d4a10ff71e6a43f6a59fa6e8548196 |
apache-2.0 | ['audio', 'TTS'] | false | some helper routines from IPython.display import Audio as IPAudio, display as IPdisplay import torch import torchaudio def play_audio(waveform, sample_rate): waveform = waveform.numpy() if len(waveform.shape)==1: IPdisplay(IPAudio(waveform, rate=sample_rate)) return num_channels, num_frames = waveform.... | 2b6d1ecd6939a08150d0146f21310313 |
apache-2.0 | ['audio', 'TTS'] | false | you can run in half mode on gpu. model = model.cuda().half() sentences = [ "Advanced text to speech models such as Fast Speech can synthesize speech significantly faster than previous auto regressive models with comparable quality. The training of Fast Speech model relies on an auto regressive teacher model fo... | d4a420df4a682555cc9dfac15146ea7a |
apache-2.0 | ['audio', 'TTS'] | false | Github Code Repo Current code for this model can be found [here](https://github.com/ontocord/fastspeech2_hf) This is a work in progress (WIP) port of the model and code from [this repo] (https://github.com/ming024/FastSpeech2). The datasets on which this model was trained: - LJSpeech: a single-speaker English data... | 1487ceec1a820cd45b7950c00cb45b16 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Model Dreambooth concept any-ely-wd-Noah_Titan-3500 ฤฦฐแปฃc train bแปi hr16 bแบฑng [Shinja Zero SoTA DreamBooth_Stable_Diffusion](https://colab.research.google.com/drive/1G7qx6M_S1PDDlsWIMdbZXwdZik6sUlEh) notebook <br> Test concept bแบฑng [Shinja Zero no Notebook](https://colab.research.google.com/drive/1Hp1ZIjPbsZKlCtomJVm... | cea4730f7d849b588bd5e96a1c4bf3d3 |
apache-2.0 | ['sexism detector'] | false | twitter_sexismo-finetuned-exist2021 This model is a fine-tuned version of [pysentimiento/robertuito-base-uncased](https://huggingface.co/pysentimiento/robertuito-base-uncased) on the EXIST dataset It achieves the following results on the evaluation set: - Loss: 0.47 - Accuracy: 0.80 - F1: 0.83 - F2: 0.89 | 060be7796453a7be86485f87ad92df78 |
apache-2.0 | ['sexism detector'] | false | Training procedure The model has been trained to get the best F2 score.The F-measure is calculated as the harmonic mean of precision and recall, giving each the same weighting. It allows a model to be evaluated taking both the precision and recall into account using a single score, which is helpful when describing the... | 610cc83b4e136efb78ca3d22000251b7 |
apache-2.0 | ['sexism detector'] | false | Training hyperparameters The following hyperparameters were used during training: - my_learning_rate = 5E-5 - my_adam_epsilon = 1E-8 - my_number_of_epochs = 8 - my_warmup = 3 - my_mini_batch_size = 32 - optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 | d9ae1ae9d1d1469f064a523a4830d557 |
apache-2.0 | ['sexism detector'] | false | Training results Epoch T. Loss V. Loss Accuracy F1 Precision Recall F2 1 0.478700 0.443148 0.804386 0.830160 0.750689 0.928450 0.886467 2 0.298000 0.460549 0.823684 0.841107 0.784661 0.906303 0.879048 3 0.063600 0.706177 0.817544 0.829508 0.799368 0.862010 0.848... | e8689a542ec96253b782f970fc35eb2f |
apache-2.0 | ['sexism detector'] | false | usage pipelines model_checkpoint = "robertou2/twitter_sexismo-finetuned-robertuito-exist2021" pipeline_nlp = pipeline("text-classification", model=model_checkpoint) pipeline_nlp("mujer al volante peligro!") | 76f95e6ad0566175e1be7214804f30c2 |
apache-2.0 | ['sexism detector'] | false | Retos Uno de los principales retos que se encontrรณ en este proceso ha sido disponer de un dataset en espaรฑol. Se ha logrado conseguir (previa solicitud) el dataset utilizado en [EXIST:sEXism Identification in Social neTworks](http://nlp.uned.es/exist2021/), el cual fue un gran punto de partida para comenzar con el mod... | 52a531da48d3b85b3fefc0442269592b |
apache-2.0 | ['sexism detector'] | false | Trabajos Futuros Se propone incrementar el dataset desarrollado. Para esto es posible descargar cantidades superiores de tweets en espaรฑol y aplicar tรฉcnicas de active learning para obtener un grupo reducido de tweets a etiquetar vรญa crowdsourcing, y en donde estos datos etiquetados puedan servir para etiquetar el res... | 1bba0ffb93679072cd1eae080913804e |
apache-2.0 | ['sexism detector'] | false | Posibles Aplicaciones Primero es sumamente importante dar mayor visibilidad al problema de _sexismo en redes sociales_, principalmente en espaรฑol. El proceso de Transfer Learning logra reutilizar y aprovechar modelos previamente entrenados, y lo que se desea es que nuevos grupos de investigaciรณn, estudiantes, etc. uti... | a4157484f8ee5dda7f78325f07e7fab7 |
apache-2.0 | ['sexism detector'] | false | Referencias 1 de Paula, A. F. M., da Silva, R. F., & Schlicht, I. B. (2021). Sexism Prediction in Spanish and English Tweets Using Monolingual and Multilingual BERT and Ensemble Models. arXiv preprint arXiv:2111.04551. Rodrรญguez-Sรกnchez, F., Carrillo-de-Albornoz, J., Plaza, L., Gonzalo, J., Rosso, P., Comet, M., & Do... | 9b2ce060b8ba9bb9b3956e7f9178604b |
creativeml-openrail-m | [] | false | isopixel-diffusion-v1 Stable Diffusion v2-768 model trained on to generate isometric pixel art <div style="display: flex; flex-direction: row; flex-wrap: wrap"> <img src="https://s3.amazonaws.com/moonup/production/uploads/1669957996471-6303f37c3926de1f7ec42d3e.png" width="256"> <img src="https://s3.amazonaws.com/m... | 5563657a24e8483791a5894c5e037b82 |
creativeml-openrail-m | [] | false | How to use - Download the model and use it on your desired UI (Tested on AUTOMATIC1111's) Currently only .ckpt version is supported - Trigger the style in your prompt with the **isopixel** token, look at the next section for more examples | afa77374083d546210580eec57559042 |
creativeml-openrail-m | [] | false | Examples **isometric bedroom, isopixel style** Steps: 50, Sampler: Euler a, CFG scale: 7.5, Size: 768x768 <img src="https://s3.amazonaws.com/moonup/production/uploads/1669958684775-6303f37c3926de1f7ec42d3e.png" width="512"/> **isometric sushi store, isopixel style** Steps: 50, Sampler: Euler a, CFG scale: 7.5, Size: ... | b2a2f1457527fe6845a908281ad34a3b |
creativeml-openrail-m | [] | false | Tips - Always use 768x768 - High step count on Euler_a gives the best results - Low CFG scale outputs great results - You can use a tool like Pixelator to achieve a better effect. This model **isn't pixel perfect** (yet ๐) Please consider supporting further research on my Patreon: <a href="https://www.patreon.com/us... | 5f446b2d5edca98c61dc6b2636f4b573 |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-patch16-224-in21k-finetuned-lora-food101 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 0.1448 - Accuracy: 0.96 | 2cbd31d000b72c4b3dec89997d3eff4b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num... | 02427c5c458bd7774e7d1c4fe8e1488d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 9 | 0.5069 | 0.896 | | 2.1627 | 2.0 | 18 | 0.1891 | 0.946 | | 0.3451 | 3.0 | 27 | 0.1448 | 0.... | 4fc4937de2d3ea7ddeb18328c4de2f14 |
apache-2.0 | ['generated_from_trainer'] | false | insertion-prop05-vocab This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0209 - Precision: 0.9815 - Recall: 0.9787 - F1: 0.9801 - Accuracy: 0.9929 | 6ce7bc6678465c52ef0808373e8f8c7a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0687 | 0.32 | 500 | 0.0275 | 0.9770 | 0.9694 | 0.9732 | 0.9904 | | 0.0327 | 0.64 |... | d716aa5282409d325a311ad4936b62df |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_cola_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6839 - Matthews Correlation: 0.0 | e3e7fdbf8af18227ae9f22117745657b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.82 | 1.0 | 34 | 0.6841 | 0.0 | | 0.7971 | 2.0 | 68 | 0.6840 | 0.0 | | 0.7... | ba6ec6b39e2131446300b053f9cf4fde |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper medium Serbian El Greco This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0,google/fleurs sr,sr_rs dataset. It achieves the following results on the evaluation set: - Loss: 0.4868 - Wer: 12.1408 | 335419246c0708623997c6cbd4e821b0 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.0222 | 2.72 | 1000 | 0.3442 | 14.0834 | | 0.0032 | 5.43 | 2000 | 0.4106 | 14.5285 | | 0.0011 | 8.15 | 3000 | 0.4331 | 1... | f8d80624927cb46e62a22ba94f7047fb |
apache-2.0 | ['generated_from_trainer'] | false | distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_12_47 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: 0.1194 - Prec... | 65cc386090d875c80f5355e4dcbad880 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 15 | 0.0877 | 0.12 | 0.0194 | 0.0333 | 0.9830 | | No log | 2.0 |... | 19f86528d7bc13081c652e3b1783eb69 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-common_voice-ur-demo-dist This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 0.5252 | 80b0019a9d9c0ba4722b5643f5e6ed8d |
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 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 8 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | aa46ceb4c8be2acbff8236f5d648bc12 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 6.0956 | 0.11 | 100 | inf | 1.0 | | 3.4569 | 0.22 | 200 | inf | 1.0 | | 3.0492 | 0.32 | 300 | inf | 0.997... | 13801f3bbf624bf413d12efeb40095d6 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples 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: 0.3568 - Accuracy: 0.86 - F1: 0.8679 | e4221e384b4b1740849e38fd5d1e95d1 |
apache-2.0 | ['translation'] | false | swe-epo * source group: Swedish * target group: Esperanto * OPUS readme: [swe-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/swe-epo/README.md) * model: transformer-align * source language(s): swe * target language(s): epo * model: transformer-align * pre-processing: normalization + Se... | 4e9432bd4657991b31a54552148b478d |
apache-2.0 | ['translation'] | false | System Info: - hf_name: swe-epo - source_languages: swe - target_languages: epo - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/swe-epo/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['sv', 'eo'] - src_constituents: {'swe'} - tgt_const... | cca8d06f54f088a26214439aa24458b7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.2469 - Accuracy: 0.9458 | 244ac8b9906a48816c36e39486b88f25 |
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