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 | [] | false | This model is a fine-tuned checkpoint of [T5-base](https://huggingface.co/t5-base). Fine-tuned on the [Wiki Neutrality Corpus (WNC)](https://github.com/rpryzant/neutralizing-bias), a labeled dataset composed of 180,000 biased and neutralized sentence pairs that are generated from Wikipedia edits tagged for “neutral po... | 9d1ad3794e8ee7ffb8ef1e6b439791dc |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-splitted-idrak-exp_test This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6360 - Wer: 1.0 | 1ec329f7134a182a217bd560737cfb67 |
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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 100 - mixed_precision_trai... | dfe2625c7351c155c1a3b484a224a5ef |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 3.4894 | 25.0 | 100 | 3.3342 | 1.0 | | 3.3573 | 50.0 | 200 | 3.5575 | 1.0 | | 3.331 | 75.0 | 300 | 3.5871 | 1.0 | | 3.3274 ... | 64d06eef9f0465909a3ea3c31c8c8a41 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.05, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.000475}, ... | ee7db51cafa7a65b293d55c279d66c9b |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_logit_kd_qnli_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 1.0610 - Accuracy: 0.5054 | 47ea1731f8d30f64781198d4b6b571c0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1395 | 1.0 | 819 | 1.0611 | 0.5054 | | 1.1393 | 2.0 | 1638 | 1.0611 | 0.5054 | | 1.1393 | 3.0 | 2457 | 1.0617 | 0.... | b18189282d7d0071b0371f56eaab7c88 |
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: 0.2237 - Accuracy: 0.9245 - F1: 0.9247 | 65d5007b58d0f122eb2045619a733e4b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8356 | 1.0 | 250 | 0.3296 | 0.901 | 0.8977 | | 0.254 | 2.0 | 500 | 0.2237 | 0.9245 | 0.9247 | | 6970b4e18d0464a455f8efdcae6dcd65 |
apache-2.0 | ['generated_from_trainer'] | false | glue_sst_classifier This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2359 - F1: 0.9034 - Accuracy: 0.9014 | 135eb45ea0ce8f4af3788660431700c2 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1.0 | b32717936a14597bb6551f9b067048d3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| | 0.3653 | 0.19 | 100 | 0.3213 | 0.8717 | 0.8727 | | 0.291 | 0.38 | 200 | 0.2662 | 0.8936 | 0.8911 | | 0.2239 |... | 7810ec0b1fc9f615ecf51fe587bd395c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_pretrain_sst2 This model is a fine-tuned version of [gokuls/distilbert_sa_pre-training-complete](https://huggingface.co/gokuls/distilbert_sa_pre-training-complete) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2580 - Accuracy: 0.911... | f9f758bbce9a5c9061895dd334dceb26 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4817 | 1.0 | 264 | 0.2580 | 0.9117 | | 0.1825 | 2.0 | 528 | 0.3411 | 0.9083 | | 0.1153 | 3.0 | 792 | 0.3009 | 0.... | 8999c9072aac24a9c643299a0d8597a2 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Turkish medium sized pipeline for TrSpaCy. Components: tok2vec, tagger, morphologizer, lemmatizer, parser, ner | Feature | Description | | --- | --- | | **Name** | `tr_core_news_md` | | **Version** | `3.4.2` | | **spaCy** | `>=3.4.2,<3.5.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `trainable_le... | 733fb3f0bf7b22cead039c706531a22e |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (1572 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `ADP`, `ADV`, `ANum`, `ANum_Adj`, `ANum_Ness`, `ANum_Noun`, `ANum_With`, `ANum_Zero`, `Abr`, `Abr_With`, `Adj`, `Adj_Ness`, `Adj_With`, `Adj_Without`, `Adj_Zero`, `Adv`, `... | 4b2a995c3f4826758e4bbbf1f0b591bf |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TAG_ACC` | 91.42 | | `POS_ACC` | 90.52 | | `MORPH_ACC` | 88.93 | | `LEMMA_ACC` | 81.72 | | `DEP_UAS` | 72.75 | | `DEP_LAS` | 63.55 | | `SENTS_P` | 85.45 | | `SENTS_R` | 81.61 | | `SENTS_F` | 83.49 | | `ENTS_F` | 88.94 | | `ENTS_P` | 88.90 | | `ENTS_R` | 88.97 | | ecd3d5d76df0a2504148da2a2e1cbb1f |
apache-2.0 | [] | false | BETO (Spanish BERT) + Spanish SQuAD2.0 + distillation using 'bert-base-multilingual-cased' as teacher This model is a fine-tuned on [SQuAD-es-v2.0](https://github.com/ccasimiro88/TranslateAlignRetrieve) and **distilled** version of [BETO](https://github.com/dccuchile/beto) for **Q&A**. Distillation makes the model *... | 2fdfb52bf293e824cbca74e02000db0e |
apache-2.0 | [] | false | Model training The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command: ```bash !export SQUAD_DIR=/path/to/squad-v2_spanish \ && python transformers/examples/distillation/run_squad_w_distillation.py \ --model_type bert \ --model_name_or_path dccuchile/bert-base-spanish-wwm-cased \ ... | 8f2a5a03560ce1b42d64b3e42e369b3a |
apache-2.0 | [] | false | Important!: By now the QA pipeline is not compatible with fast tokenizer, but they are working on it. So that pass the object to the tokenizer {"use_fast": False} as in the following example: nlp = pipeline( 'question-answering', model='mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es', ... | 81b45e4f654188a9846fbd24d51f3ba7 |
apache-2.0 | [] | false | Output: {'answer': 'español', 'end': 169, 'score': 0.67530957344621, 'start': 163} ``` Play with this model and ```pipelines``` in a Colab: <a href="https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/Using_Spanish_BERT_fine_tuned_for_Q%26A_pipelines.ipynb" target="_parent"><img src="... | b4756507c502c1b5f308749d11968860 |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq'] | false | T5-small-nl16 for Finnish Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in [this paper](https://arxiv.org/abs/1910.10683) and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer). **Note:** The Hug... | ef8cf7138aa34fb72beec6bb9b767c5b |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq'] | false | t511) improvements compared to the original T5 model during the pretraining: - GEGLU activation in feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202) - Dropout was turned off in pretraining (quality win). Dropout should be re-enabled during fine-tuning - Pretrained on span-based ... | 43e5a632167961bf06dd0f25352cabf9 |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq'] | false | How to use Here is how to use this model in PyTorch: ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("Finnish-NLP/t5-small-nl16-finnish") model = T5ForConditionalGeneration.from_pretrained("Finnish-NLP/t5-small-nl16-finnish") ``` and in TensorFlow:... | 54e20bb66132708a2888608b7d0cecf7 |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq'] | false | Pretraining The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 500K steps with a batch size of 256 (in total 66B tokens). The optimizer used was a AdaFactor with learning rate warmup for 10K steps with a constant learning rate of 1e-2, and ... | 398d5386f9df35cb6033082b21baf0b5 |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq'] | false | Evaluation results Evaluation was done by fine-tuning the model on a downstream text classification task with two different labeled Finnish datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Classification fine-tuning was done with a sequence length... | aa57dc05e81e943995ee6718e0e30716 |
mit | [] | false | ned-flanders on Stable Diffusion This is the `<flanders>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also... | e2e6e78f684cf5972b056b633759e1eb |
apache-2.0 | ['generated_from_trainer'] | false | kadoa-page-extraction This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8235 | caa075bfab9ea4b2ab1702b978264ac5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 1 | 0.8235 | | No log | 2.0 | 2 | 0.8235 | | No log | 3.0 | 3 | 0.8235 | | No log | 4.0 | 4 | 0.8235 ... | 03b3810f68fdb6bda47616ca813c3c7f |
gpl-3.0 | ['pytorch', 'token-classification', 'albert', 'zh'] | false | CKIP ALBERT Tiny Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 | 0c30c45b927e8991f69a3af2872054de |
gpl-3.0 | ['pytorch', 'token-classification', 'albert', 'zh'] | false | Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-tiny-chinese-pos'... | 5a1fbc3f6d6cbb6f4e7bf501de34e272 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-base-zle-bat Neural machine translation model for translating from East Slavic languages (zle) to Baltic languages (bat). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many langu... | 3af5cb5c72c36dd88ff7144e35069894 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-14 * source language(s): rus * target language(s): lav lit * valid target language labels: >>lav<< >>lit<< * model: transformer-align * data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opu... | b022155470157974d5fa746c1e782e14 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>lav<< Африка - колыбель человечества.", ">>lit<< Том — наш капітан." ] model_name = "pytorch-models/opus-mt-tc-base-zle-bat" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMT... | 30d0e481081e42b46159766a62dd75a4 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Tomas yra mūsų kapitonas. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-zle-bat") print(pipe(">>lav<< Африка - колыбель человечества.")) | 75b054f1c51708ea676f2a7689f4e52e |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807_transformer-align_2022-03-14.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-bat/opusTCv20210807_transformer-align_2022-03-14.test.txt) * test set scores: [opusTCv20210807_transformer-align_2022-03-14.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-mod... | d0da080f8526c87a0f74234f8b98594f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | rus-lav | tatoeba-test-v2021-08-07 | 0.74223 | 55.3 | 274 | 1518 | | rus-lit | tatoeba-test-v2021-08-07 | 0.70795 | 47.2 | 3598 | 20662 | | rus-lav | flores101-devtest | 0.50134 | 20.0 | 1012 | 22092 | | rus-lit | flores101-devtest | 0.53732 | 20.6 | 101... | 30665982debd1b852b13c04976241911 |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/t5-large-subjqa-grocery-qg` This model is fine-tuned version of [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: grocery) via [`lmqg`](https://github.com/asahi417/lm-... | aa81118c1818c28198a4825ecd00d256 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (grocery) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/as... | a05ed18f7de6c0c17ffaf3ab3e1699dd |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-large-subjqa-grocery-qg")... | edc3d82c6ce8e1fa7edfa6a582ce3e0d |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-large-subjqa-grocery-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.grocery.json) | | Score | Type | Dataset | ... | 61ad977672cd0a3b5693ea967a7b8912 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: grocery - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: lmqg/t5-large-squad - max_length: 512 - max_length_output: 32 - epoch: 3... | c051a82b992bfe12a96574bf682fa714 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | pt_core_news_sm Portuguese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler. | Feature | Description | | --- | --- | | **Name** | `pt_core_news_sm` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline**... | 47246c0984eaf8951f2c901ccb15f7c1 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (590 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`morphologizer`** | `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Mas... | 1971ee58b50a92440997983e37019c3d |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 100.00 | | `TOKEN_P` | 99.88 | | `TOKEN_R` | 99.95 | | `TOKEN_F` | 99.92 | | `POS_ACC` | 96.24 | | `MORPH_ACC` | 94.71 | | `MORPH_MICRO_P` | 97.38 | | `MORPH_MICRO_R` | 96.78 | | `MORPH_MICRO_F` | 97.08 | | `SENTS_P` | 92.75 | | `SENTS_R` | 94.91 | | `SENTS_F` |... | 74c94ed223fd2ec04cf642b0566cd4a4 |
creativeml-openrail-m | ['text-to-image'] | false | taras Dreambooth model trained by duben with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-512 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blo... | 489771c18b3589c32973927613fe7a0c |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-NL48 (Deep-Narrow version) T5-Efficient-BASE-NL48 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... | 29c14969d0acd8f33c6b9c1f6cd96b9a |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-nl48** - is of model type **Base** with the following variations: - **nl** is **48** It has **817.7** million parameters and thus requires *ca.* **3270.79 MB** of memory in full precision (*fp32*) or **1635.39 MB** of memory in half precision (... | 927a36b523e142e4b693c078fbd84975 |
cc-by-4.0 | [] | false | TamilBERT GujaratiBERT is a Gujarati BERT model trained on publicly available Gujarati monolingual datasets. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindB... | 02e4e795827545ea9724229c6953067c |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de 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: 0.1334 - F1: 0.8654 | a04038c470cc5243ca1b0a7127098c8f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2541 | 1.0 | 525 | 0.1596 | 0.8242 | | 0.1284 | 2.0 | 1050 | 0.1360 | 0.8499 | | 0.0827 | 3.0 | 1575 | 0.1334 | 0.8654 | ... | a74af36c2cd4dd8da844d5f6ec1dc6e8 |
mit | ['generated_from_keras_callback'] | false | codeparrot-ds This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 9.8843 - Epoch: 0 | ee1bf80dd96244196a002fe9dd18ea9c |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps... | 1807dfe7de564497f34d17ec06f889ca |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_finetuned-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: 0.7799 - Accuracy: 0.9161 | d8378fac4089b86d918b98323b138529 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 318 | 3.2788 | 0.7371 | | 3.7785 | 2.0 | 636 | 1.8739 | 0.8358 | | 3.7785 | 3.0 | 954 | 1.1618 | 0.... | 62271f22027079511b68e5c377d61533 |
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: 0.2324 - Accuracy:: 0.9205 - F1:: 0.9208 | 594e48381cff8be9d823b70cd4bcd7a8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy: | F1: | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | 0.8439 | 1.0 | 250 | 0.3313 | 0.9025 | 0.8997 | | 0.2538 | 2.0 | 500 | 0.2324 | 0.9205 | 0.9208 | | a9bcf7f414486e620dc317d1f149b9aa |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 128, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, ... | 3414eca0dabd75e9e289f9339421880e |
other | [] | false | Carpet Cleaning Addison Texas http://carpetcleaningaddison.com/ (972) 379-7364 Private floor covering cleaners will go to your home when expected to give you various administrations, for example, cover stain expulsion, profound rug cleaning, and one end to the other rug cleaning. A few stains become long-lasting sooner... | ca21aa1cfca992aa4523dea7b1d3104a |
other | [] | false | 1 for the most ideal outcomes that anyone could hope to find. Cover Cleaning Addison Texas is consistently on first in class with the most recent tests and updates for all important rug medicines, we are 100 percent sure that our tried cleaning items which have set us in the number 1 position will leave with only total... | bebff3435ce25009de0e9581ce9cbd20 |
mit | ['generated_from_trainer'] | false | GPT-Y This model is a fine-tuned version of [juancopi81/gpt2-finetuned-yannic-large](https://huggingface.co/juancopi81/gpt2-finetuned-yannic-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0797 | 8de05bb7e8f4575c84a22ae478d8189c |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 | d7d0dcba782118b15e1bbf5b8535987c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 403 | 3.0809 | | 2.9847 | 2.0 | 806 | 3.0811 | | 2.9516 | 3.0 | 1209 | 3.0781 | | 2.916 | 4.0 | 1612 | 3.0791 ... | cfacb65095953bc10dd65d4723a9d207 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | ◆ EmiPhaV4 sample EmiPhaV4はプロンプト、ネガティブプロンプトを長くすると色褪せる傾向があるのでできるだけ短めのプロンプトが良いかもしれません  ``` Prompt: 1 girl,cat ears, maid, Negative: (worst quality,low quality,monochrome:1.2),(bad hands:1.3), Steps: 30 Sampler: DPM++ 2M Karras CFG Scale: 9 Siz... | bf18befcc2ca6e040753ad87854de648 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | ◆ EmiPhaV3 sample EmiPhaV3はプロンプト、ネガティブプロンプトを短くすると逆に色が濃く出るので、ある程度長いプロンプトの方が良い感じになるかもしれません。  ``` Prompt: 1 girl,cat ears, maid, Negative: (worst quality,low quality,monochrome:1.2),(bad hands:1.3), Steps: 30 Sampler: DPM++ 2M Karras CFG Scale:... | 068708ea843b0ff4f4674a4dfd003f07 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | 【和訳】 このモデルはオープンアクセスであり、すべての人が利用できます。CreativeML OpenRAIL-M ライセンスにより、権利と使用方法がさらに規定されています。CreativeML OpenRAIL ライセンスでは、次のことが規定されています。 1. モデルを使用して、違法または有害な出力またはコンテンツを意図的に作成または共有することはできません。 2. 作成者は、あなたが生成した出力に対していかなる権利も主張しません。あなたはそれらを自由に使用でき、ライセンスに設定された規定に違反してはならない使用について説明責任を負います。 3. 重みを再配布し、モデルを商用および/またはサービスとして使用することがで... | aa86423144e60be25e54396152c235dd |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 17089cb2cf5f1275132163f6327defbcc1b1bc1b pip install -e . cd egs2/iemocap/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_wav2vec2_2 ``` | 5ef02f3d80d74025077516dd2f54c9fb |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_wav2vec2_2.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_wav2vec2_2_raw_en_word ngpu: 1 seed: 2022 num_workers: 2 num_att_plot: 3 dist_backend: nccl dist... | dd03e4c099050eadccf66cf519f7646c |
apache-2.0 | ['translation'] | false | en-de * source group: English * target group: German * OPUS readme: [eng-deu](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-deu/README.md) * model: transformer-big * source language(s): eng * target language(s): deu * raw source language(s): eng * raw target language(s): deu * model: t... | 8893346d9f5ba47268cd01d30eda7961 |
apache-2.0 | ['translation'] | false | words | BP | |---------|-------|-------|-------|--------|----| | newssyscomb2009.eng-deu | 24.3 | 0.5462 | 502 | 11271 | 0.993 | | news-test2008.eng-deu | 24.7 | 0.5412 | 2051 | 47427 | 1.000 | | newstest2009.eng-deu | 23.6 | 0.5385 | 2525 | 62816 | 0.999 | | newstest2010.eng-deu | 26.9 | 0.5589 | 248... | f59e00d8e4793a2fc00be84e2640b026 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: en-de - source_languages: eng - target_languages: deu - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-deu/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'de'] - src_constituents: ('English', {'eng'}) - tgt_co... | 03cbc621cccd51757a4652c58ed4a0f2 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-base-squad-qg-no-answer` This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-genera... | bb6933c60ebfed0e2b497b78a1776af4 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-base-squad-qg-... | 6b0cc04d512a6a1d77a6e93b3447b0e4 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-base-squad-qg-no-answer/raw/main/eval/metric.first.sentence.paragraph_sentence.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset ... | 8fc6ebb7e45285cd8f6dd5ea195789b1 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-base - max_length: 512 - max_length_output: 32 - epoch: 8 - batch: ... | 3ebab918c67668b5df756de539bfc2ab |
creativeml-openrail-m | [] | false | Usage Can be used in StableDiffusion, including the extremely popular Web UI by Automatic1111, like any other model by placing the .CKPT file in the correct directory. Please consult the documentation for your installation of StableDiffusion for more specific instructions. Use the following tokens in your prompt to a... | 2e1cf857a4a4ce88ae795381e9c08cfd |
other | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | segformer-b5-finetuned-magic-cards-230117 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the andrewljohnson/magic_cards dataset. It achieves the following results on the evaluation set: - Loss: 0.2096 - Mean Iou: 0.6629 - Mean Accuracy: 0.9944 - Overall Accuracy: 0.9944... | 16c78b7b2ec31a795fa2ad28cf26671f |
other | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 98507c4c028e7332dff23383c5357445 |
other | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Front | Accuracy Back | Iou Unlabeled | Iou Front | Iou Back | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------... | ce6ba6a7a01676b6f1a5156fb2d0c7fb |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | da_core_news_trf Danish transformer pipeline (Maltehb/danish-bert-botxo). Components: transformer, morphologizer, parser, lemmatizer (trainable_lemmatizer), ner, attribute_ruler. | Feature | Description | | --- | --- | | **Name** | `da_core_news_trf` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **De... | 12671a09e56210298503563e0269017c |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | danish-dependency-treebank-dane) (Rasmus Hvingelby, Amalie B. Pauli, Maria Barrett, Christina Rosted, Lasse M. Lidegaard, Anders Søgaard)<br />[Maltehb/danish-bert-botxo](https://huggingface.co/Maltehb/danish-bert-botxo) (BotXO.ai) | | **License** | `CC BY-SA 4.0` | | **Author** | [Explosion](https://explosion.ai) | | ef6cd4e1d5a5903013a06e7646454169 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (193 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`morphologizer`** | `AdpType=Prep\|POS=ADP`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PROPN`, `Definite=Ind\|Num... | e2a3d2257ad5c14eb0adb9f9751ca6a7 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.89 | | `TOKEN_P` | 99.78 | | `TOKEN_R` | 99.75 | | `TOKEN_F` | 99.76 | | `POS_ACC` | 97.67 | | `MORPH_ACC` | 97.36 | | `MORPH_MICRO_P` | 98.72 | | `MORPH_MICRO_R` | 97.89 | | `MORPH_MICRO_F` | 98.30 | | `SENTS_P` | 85.84 | | `SENTS_R` | 85.99 | | `SENTS_F` | ... | d74940ddc4b0080682c8b7229787039c |
mit | ['generated_from_trainer'] | false | bart-large-cnn-finetuned-roundup-16 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8957 - Rouge1: 49.4097 - Rouge2: 29.3516 - Rougel: 31.527 - Rougelsum: 46.4241 -... | f3ae411dff2101579115eed7a7c63993 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 132 | 1.3170 | 48.412 | 29.2017 | 31.6679 | 45.494 | 14... | 9f7e3e4fea3bfcf1d6ac4c3117f3b2d9 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-finetuned-multi-news1 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the multi_news dataset. It achieves the following results on the evaluation set: - Loss: 2.0858 - Rouge1: 42.1215 - Rouge2: 14.9986 - Rougel: 23.4737 - Rougelsum: 36.... | cf25f024baa97fea5dbe1a90eb20a40d |
mit | ['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 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoc... | 94c935503fa42895bd934073b06b5d5e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.1984 | 1.0 | 750 | 2.0858 | 42.1215 | 14.9986 | 23.4737 | 36.4212 | 13... | 6a787164b07358d292163f1a08c73ebf |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_xlsr-53_s756 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech... | 07872c3605f243ed38cb1418d5920fd7 |
apache-2.0 | [] | false | Wav2Vec2 model HPU configuration This model only contains the `GaudiConfig` file for running the [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) model on Habana's Gaudi processors (HPU). **This model contains no model weights, only a GaudiConfig.** This enables to specify: - `use_habana_mixed_precision`: ... | c4abe2aac71e85c14e56dfa1881ac2d4 |
apache-2.0 | [] | false | Usage The model is instantiated the same way as in the Transformers library. The only difference is that there are a few new training arguments specific to HPUs. [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/audio-classification/run_audio_classification.py) is an audio classification exampl... | c8e43cb84244110c031a4580adec5b78 |
apache-2.0 | ['generated_from_trainer'] | false | CTEBMSP_bsc_test This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0557 - Diso Precision: 0.8917 - Diso Recall: 0.8968 - Diso F1: 0.8943 - Diso Number: 2645... | 3d15814cdba86aa480fc9122756d0704 |
apache-2.0 | ['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 | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:... | 2a4abddf3e43088057e9e04bcf0b80f9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbart-cnn-12-6-summarization_final_labeled_data This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0858 - Rouge1: 76.5974 - Rouge2: 66.1659 - Rougel: 71... | ebfe5d3bb4c875621803d502cd61bdb7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 99 | 0.2852 | 61.0841 | 45.81 | 52.9835 | 59.0452 | 11... | aeebbe1a40ecd55ace6d62beb0b95df4 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-mlm-pubmed-45 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6395 - Rouge2 Precision: 0.3383 - Rouge2 Recall: 0.2424 - Rouge2 Fmeasure: 0.2753 | c9a99e9913be86812a22961544095423 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 2.519 | 0.75 | 500 | 1.9659 | 0.3178 | 0.1888 | 0.2299 ... | 4859369a3d78b1bb06047f9a8c267040 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxvit_nano_rw_256.sw_in1k A timm specific MaxViT image classification model. Trained in `timm` on ImageNet-1k by Ross Wightman. ImageNet-1k training done on TPUs thanks to support of the [TRC](https://sites.research.google/trc/about/) program. | f5d14ea5a31ebe263f0643ecc5f9cfbd |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 15.5 - GMACs: 4.5 - Activations (M): 30.3 - Image size: 256 x 256 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - **Dataset:** ImageNet-1k | dc9539f9ca6a85f90c8ae49bdd086acd |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('maxvit_nano_rw_256.sw_in1k', pretrained=True) model = ... | 2eac3253defecece8be3aeb10c7384dc |
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