license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['recsys', 'pytorch', 'sentence_transformers'] | false | Paper & samples
The overall idea for `paper-rec` test model is inspired by this work: [NU:BRIEF – A Privacy-aware Newsletter Personalization Engine for Publishers](https://arxiv.org/abs/2109.03955).
However, for `paper-rec`, we use a different language model more suitable for longer text, namely *Sentence Transfor... | 670e73fb3cdbe764de62be4d74761d2e |
mit | ['recsys', 'pytorch', 'sentence_transformers'] | false | Data
The data used for this model corresponds to the [RSS news feeds for arXiv updates](https://arxiv.org/help/rss) accessed on 2022-02-04. In particular to the ones related to Machine Learning and AI:
1. [Artificial Intelligence](http://arxiv.org/rss/cs.AI)
1. [Computation and Language](http://arxiv.org/rss/cs.... | 8e107253368036cc601e7bf5b051b3ae |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_sc... | 76e482e34a8256ca7943a9c6311b79a9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.96 | 16 | 10.3553 | | No log | 1.96 | 32 | 9.5625 | | No log | 2.96 | 48 | 9.0898 | | No log | 3.96 | 64 | 8.7852 ... | 1d76837dea1a0dc55fefc109646c25f5 |
apache-2.0 | ['generated_from_keras_callback'] | false | cjjie/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: 0.7784 - Epoch: 1 | e6c418bc02771a1cdd8dc0d35e2f008e |
apache-2.0 | ['generated_from_trainer'] | false | XLS-R_Finetuned This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2280 - Wer: 0.1725 | bee6bede33ba33d4ce297394e2e63a86 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00024 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | dda96efd08c1dbee730b88ac99abbb87 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 6.0094 | 0.32 | 500 | 3.5637 | 1.0 | | 3.3935 | 0.64 | 1000 | 2.6589 | 1.0 | | 1.5455 | 0.95 | 1500 | 0.7979 | 0.822... | 5b917f13283752acc4dc774d381fb7ed |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-qnli This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8121 - Accuracy: 0.6065 | 3ccd10db8fc746ee29903c5d4dd63664 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 156 | 0.6949 | 0.4874 | | No log | 2.0 | 312 | 0.6596 | 0.5957 | | No log | 3.0 | 468 | 0.7186 | 0.... | 23f022d402b57f88f87d5d3b4cdf9e31 |
mit | ['graphs'] | false | Model Sources <!-- Provide the basic links for the model. --> - **Repository:** [Github](https://github.com/microsoft/Graphormer) - **Paper:** [Paper](https://arxiv.org/abs/2106.05234) - **Documentation:** [Link](https://graphormer.readthedocs.io/en/latest/) | d01c5e0bf031cd2a52aa07f6024706ad |
mit | ['graphs'] | false | Direct Use This model should be used for graph classification tasks or graph representation tasks; the most likely associated task is molecule modeling. It can either be used as such, or finetuned on downstream tasks. | e0b74637a0c07fc2c91dfb208e829288 |
mit | ['graphs'] | false | Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** ``` @article{DBLP:journals/corr/abs-2106-05234, author = {Chengxuan Ying and Tianle Cai and Shengjie Luo and ... | 7cd00eff665cc6b7339a28de76ae10fc |
apache-2.0 | ['generated_from_keras_callback'] | false | tmp9eavpdw4 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1333 - Train Accuracy: 0.9487 - Validation Loss: 0.7282 - Validation Accuracy: 0.7929 - Epoch: 1 | 4e219141621639f5af8d8f10851f411b |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.3768 | 0.8296 | 0.4746 | 0.8159 | 0 | | 0.1333 | 0.9487 | 0.7282 | 0.7929 ... | f5a9c5f0fdb3580a906c9f1757231785 |
cc-by-sa-4.0 | ['erzya', 'mordovian', 'translation'] | false | This a model to translate texts from the Erzya language (`myv`, cyrillic script) to 11 other languages: `ru,fi,de,es,en,hi,zh,tr,uk,fr,ar`. See its [demo](https://huggingface.co/spaces/slone/myv-translation-2022-demo)! It is described in the paper [The first neural machine translation system for the Erzya language](h... | 2fd128a879ac8047ca2fb032b45b51ed |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_logit_kd_stsb_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 1.1505 - Pearson: 0.0470 - Spearmanr: 0.0414 - Combined Score:... | 27855c09cfa36739aa788610820240e4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 2.524 | 1.0 | 45 | 1.3607 | -0.0066 | -0.0281 | -0.0174 | | 1.0877 | 2.0 | 90 ... | 82c18fb1bbf6cecc4fed1aff037caa56 |
cc-by-4.0 | [] | false | Concept Art style with no copyright restriction (Attribution would be nice but not necessary) Prompt: john walker lee Example: john walker lee style, realistic people in post apocalyptic city, holding flowers, cinematic, kodachrome, textured, dramatic lighting, scratches ![John Walker Lee Style 000202.d9f495d2.2015... | 35e3cc38e7b1eef757da0b0a7247511d |
mit | ['conversational'] | false | How to use Now we are ready to try out how the model works as a chatting partner! ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("microsoft/GODEL-v1_1-large-seq2seq") model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/GODEL-v1_1-large-seq2seq")... | 2fb8b6d2262ac8a8f8bed11e6771ee4b |
apache-2.0 | ['Super-Resolution', 'computer-vision', 'ESRGAN', 'gan'] | false | BSRGAN Usage ```python from bsrgan import BSRGAN model = BSRGAN(weights='kadirnar/RRDB_PSNR_x4', device='cuda:0', hf_model=True) model.save = True pred = model.predict(img_path='data/image/test.png') ``` | 61e2220458f57371a9c01ee0390bf86f |
apache-2.0 | ['generated_from_keras_callback'] | false | ririying/mt5-small-finetuned-mt5-class1 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.0908 - Validation Loss: 1.7689 - Epoch: 7 | 311c3b8f7804e177154777e208970a9e |
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': 5.6e-05, 'decay_steps': 71320, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'deca... | f075b286f2b81503c5f7495be9be2087 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.8999 | 2.2395 | 0 | | 2.6457 | 1.9951 | 1 | | 2.3865 | 1.8784 | 2 | | 2.2622 | 1.8179 | 3 | | 2.1877 | 1.7959 | 4 | | 2.1395 |... | 0cc1b162fa0e3c3d2bb5a6796eaaefd3 |
mit | ['text2text generation'] | false | TL;DR **Our [full model](https://huggingface.co/haining/scientific_abstract_simplification) is out!🎉🎉🎉 It leverages the power of multi-instruction finetuning and beats the baseline by a margin. Use the [full model](https://huggingface.co/haining/scientific_abstract_simplification) unless the goal is comparison.** ... | fda7a0089565c0b1c39de1a640c879f4 |
mit | ['text2text generation'] | false | Model Description Open science has significantly lowered the barriers to scientific papers. However, reachable research does not mean accessible knowledge. Scientific papers are usually replete with jargon and hard to read. A lay audience would rather trust little stories on social media than read scientific papers.... | 506a0cac1760fbdfa435c19e3f941a5b |
mit | ['text2text generation'] | false | Usage Use the code below to get started with the model. Remember to prepend the `INSTRUCTION` for best performance. ```python import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM INSTRUCTION = "summarize, simplify, and contextualize: " tokenizer = AutoTokenizer.from_pretrained("haining/sas_bas... | 6901b6ef1c3dda26e1eeb83387af714c |
mit | ['text2text generation'] | false | Test Tokens | Automated Readability Index (std.) | |----------------------------------|-----------------------------|-------------------|---------------------|---------------|------------------------------------| | Abstract | 3030/200/200 | 707,071 | 45,697 ... | 37dba78de3376101829ebd6e8843c190 |
mit | ['text2text generation'] | false | Setup We finetuned the base model with a standard language modeling objective: the abstracts are sources and the significance statements are targets. We inform the model with a task-spcific prefix ("summarize, simplify, and contextualize: ") during training. The training took roughly 9 hours on two NVIDIA RTX A5000 (... | 50a26fc4b5a699bb4b4d97e2b7eaa73d |
mit | ['text2text generation'] | false | Metrics - [SacreBLEU](https://huggingface.co/spaces/evaluate-metric/sacrebleu): SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores. Inspired by Rico Sennrich’s multi-bleu-detok.perl, it produces the official WMT scores but works with plain text. It also knows all the st... | d04aef71692ede7b3f04be2e23339eae |
mit | ['text2text generation'] | false | Results We tested our model on the SAS test set (200 samples). We generate 10 lay summaries based on each sample's abstract. During generation, we used top-p sampling with p=0.9. The mean performance is reported below. | Metrics | SAS-baseline | |----------------|-------------------| | SacreBLEU↑ | ... | 35e075bf7085ca581310eec0adc4fa43 |
mit | ['text2text generation'] | false | Disclaimer This model is created for making scientific abstracts more accessible. Its outputs should not be used or trusted outside of its scope. There is no guarantee that the generated text is perfectly aligned with the research. Resort to human experts or original papers when a decision is critical. | c974df45a4e26d8237a670c9b156c5bb |
mit | ['text-classification', 'generated_from_trainer'] | false | deberta-v3-large-finetuned-syndag-multiclass-not-gpt2-arxiv This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0272 - F1: 0.9941 - Precision: 0.9941 - Recall: 0.... | cfe75e20fa284b0695e1db8587c7dce0 |
mit | ['text-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | |:-------------:|:-----:|:-----:|:---------------:|:------:|:---------:|:------:| | 0.0213 | 1.0 | 10853 | 0.0309 | 0.9945 | 0.9945 | 0.9945 | | 12fd34b61f7a085abdf5bfc93af3a5ab |
mit | [] | false | colossus on Stable Diffusion This is the `<colossus>` 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 tra... | a6ec49e1642931c7c98d7c17abb78200 |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_unispeech-ml_s658 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When... | f68732c37b74f1471a9f7bbca439d9f6 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-pointer-adv-mtop This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the mtop dataset. It achieves the following results on the evaluation set: - Loss: 0.1341 - Exact Match: 0.5817 | ff5e9b5e1572e47fa8951e5b2dd41f07 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | |:-------------:|:-----:|:----:|:---------------:|:-----------:| | 2.1628 | 1.09 | 200 | 0.7205 | 0.0022 | | 1.1208 | 2.17 | 400 | 0.6393 | 0.0013 | | 0.8675 | 3.26 | 600 | 0.5905 ... | db152d3dbe79f5070e9d1f78199eff7d |
apache-2.0 | ['hf-course', 'generated_from_trainer'] | false | distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.6694 - Accuracy: 0.82 | 691295836c40bba1f83a60ef3d0480c9 |
apache-2.0 | ['hf-course', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | fa54080d93b1d4ec6919452c948ca8d0 |
apache-2.0 | ['hf-course', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.99 | 56 | 1.9426 | 0.5 | | No log | 1.99 | 112 | 1.4157 | 0.63 | | No log | 2.99 | 168 | 1.1351 | 0.... | b58d74d823646d5d6506a77b9172bdde |
mit | ['generated_from_keras_callback'] | false | Sushant45/2008_Sichuan_earthquake-clustered This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5065 - Train End Logits Accuracy: 0.8924 - Train Start... | 8fdd178859923d53a82c76f0a4ba4d3e |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | d6cabdd8d931598182dd63c0b1ebb56e |
apache-2.0 | ['generated_from_trainer'] | false | resnet-50-4-32 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.9705 - Accuracy: 0.6410 | a3fa204b8c3ab83e5d381c3296c21173 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 5c184fbdfb74286894de9eee0394df90 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3833 | 1.0 | 224 | 1.2683 | 0.5134 | | 1.2404 | 2.0 | 448 | 1.1342 | 0.5659 | | 1.1492 | 3.0 | 672 | 1.0359 | 0.... | f7472e32965e69e4453b73bca1b0d6ff |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 157 | 2.4898 | | No log | 2.0 | 314 | 2.4230 | | No log | 3.0 | 471 | 2.4354 | | 8c9e6f970668f270bbe33c641a2c03b5 |
apache-2.0 | ['translation'] | false | opus-mt-fr-kqn * source languages: fr * target languages: kqn * OPUS readme: [fr-kqn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-kqn/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | c9d692f3b6f53a4b8c30c5abb0414177 |
mit | ['text-classification', 'generated_from_trainer'] | false | deberta-v3-large-finetuned-paws-paraphrase-detector Feel free to use for paraphrase detection tasks! This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the paws dataset. It achieves the following results on the evaluation set: - Loss: 0.3046 - F1:... | 1c4470aacd141da2d09945a42b2442e5 |
mit | ['text-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | |:-------------:|:-----:|:-----:|:---------------:|:------:|:---------:|:------:| | 0.1492 | 1.0 | 6176 | 0.1650 | 0.9537 | 0.9385 | 0.9695 | | 0.1018 | 2.0 | 12352 | 0.1968 | 0.9... | 8a64fb0cea7bf28f2617b99e51b4c033 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner 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: - Loss: 0.1196 - Precision: 0.7872 - Recall: 0.8292 - F1: 0.8077 - Accuracy: 0.9722 | 6475e58b8541b83ce69415073e9c2b71 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1243 | 1.0 | 1380 | 0.0932 | 0.6752 | 0.8222 | 0.7415 | 0.9635 | | 0.0624 | 2.0 |... | 761358f0ebd5aa423e3a2682c1f17c77 |
other | [] | false | Air Vent Cleaning Irving TX https://carpetcleaninginirving.com/air-vent.html (214) 744-3341 Our capacity to concentrate on the contentment of our clients is one of the ways that we outperform our rivals.Every time we provide services to our customers, we take the time to do it right.We plan our appointments so that o... | 92dc7814a46bd991cf6d5d2feb20f0f9 |
apache-2.0 | ['generated_from_trainer'] | false | nmt-mpst-id-en-lr_0.0001-ep_10-seq_128_bs-32 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: 2.2914 - Bleu: 0.0708 - Meteor: 0.2054 | e23d0af2805b6c3d2fd29034b2d0fc01 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 9653992474f8ebbc6334d146e72635c9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | No log | 1.0 | 202 | 2.8210 | 0.0313 | 0.1235 | | No log | 2.0 | 404 | 2.6712 | 0.0398 | 0.1478 | | 3.0646 | 3.0 |... | abdd44c4b53ea44125749f008fd2a127 |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-DogSick This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3041 - Acc: {'accuracy': 0.6102564102564103} - F1: {'f1': 0.5980148081337936} | 2f8dbda6f11050bc88481373b9638446 |
apache-2.0 | ['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: 8 - mixed_precision_training: Native AMP | e57f0f7525b50191a9039cdc352ff12f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Acc | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:--------------------------:| | 2.4055 | 0.61 | 50 | 2.2086 | {'accuracy': 0.417... | 33b39ee9b5f7d51bbb3b72308231526a |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-wnli This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6917 - Accuracy: 0.5634 | aaccd5590e66266215116949ab273807 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 5 | 7dfb067a08141cfb596c866d7e80bffe |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 10 | 0.6925 | 0.5493 | | No log | 2.0 | 20 | 0.6917 | 0.5634 | | No log | 3.0 | 30 | 0.6971 | 0.... | 7dd31fd87311298c37a549570f9818d3 |
apache-2.0 | ['generated_from_trainer'] | false | underline_to_emphasis_model 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: nan - Rouge1: 0.1835 - Rouge2: 0.0654 - Rougel: 0.1525 - Rougelsum: 0.1523 - Gen Len: 18.4918 | 126b56aa9b09fb799e90f99363828c01 |
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: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP | 2af9f2bfbc12fd9f18e86b61818d9321 |
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 | 35 | nan | 0.1835 | 0.0654 | 0.1525 | 0.1523 | 18.4918 | |... | 84657fa4434fb0160ec00016b6ba433a |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_xls-r_accent_germany-2_austria-8_s543 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 su... | 1f4cd29725187c4d338263ca6842e3b6 |
apache-2.0 | [] | false | mFLAG mFLAG is a sequence-to-sequence model for multi-figurative language generation. It was introduced in the paper [Multi-Figurative Language Generation](https://arxiv.org/abs/2209.01835) paper by [Huiyuan Lai](https://laihuiyuan.github.io/) and [Malvina Nissim](https://scholar.google.nl/citations?user=hnTpEOAAAAAJ&... | fdbe9d8a66768e158cdad79fdedca239 |
apache-2.0 | [] | false | Model description mFLAG is a sequence-to-sequence model for multi-figurative language generation. It is trained by employing a scheme for multi-figurative language pre-training on top of BART, and a mechanism for injecting the target figurative information into the encoder; this enables the generation of text with the... | 4734591751a4bc71ee073b7f7b04bc4a |
apache-2.0 | [] | false | How to use ```bash git clone git@github.com:laihuiyuan/mFLAG.git cd mFLAG ``` ```python from model import MultiFigurativeGeneration from tokenization_mflag import MFlagTokenizerFast tokenizer = MFlagTokenizerFast.from_pretrained('laihuiyuan/mFLAG') model = MultiFigurativeGeneration.from_pretrained('laihuiyuan/mFLAG')... | 07508524559d1ea63f3709ca8769383a |
apache-2.0 | [] | false | hyperbole to sarcasm inp_ids = tokenizer.encode("<hyperbole> I am not happy that he urged me to finish all the hardest tasks in the world", return_tensors="pt") fig_ids = tokenizer.encode("<sarcasm>", add_special_tokens=False, return_tensors="pt") outs = model.generate(input_ids=inp_ids[:, 1:], fig_ids=fig_ids, forced... | 8e06189babd84f23a4e4163f3e739cc7 |
apache-2.0 | [] | false | Citation Info ```BibTeX @inproceedings{lai-etal-2022-multi, title = "Multi-Figurative Language Generation", author = "Lai, Huiyuan and Nissim, Malvina", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = October, year = "2022", address = "Gyeong... | ff594da610266c8e66bf9508976bb9c7 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'newspaper', 'ilgiornale', 'repubblica', 'headline-generation'] | false | IT5 Base for News Headline Generation 🗞️ 🇮🇹 This repository contains the checkpoint for the [IT5 Base](https://huggingface.co/gsarti/it5-base) model fine-tuned on news headline generation on the Italian HeadGen-IT dataset as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italia... | 8a1786d75bc7f80df56364cd89e33d3a |
apache-2.0 | ['italian', 'sequence-to-sequence', 'newspaper', 'ilgiornale', 'repubblica', 'headline-generation'] | false | Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines hg = pipeline("text2text-generation", model='it5/it5-base-headline-generation') hg("Arriva dal Partito nazionalista basco (Pnv) la confe... | 15d7930a0a372db07424cf3cabcf095b |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-burak-new-300-v2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.7018 - Wer: 0.3641 | 23e0ee1c1508d7e463eece73b1b89be0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - 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: 500 - num_epochs: 141 | e2e78ffa85a49fad8f57a460fb4cdb68 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 4.2967 | 8.62 | 500 | 1.0561 | 0.8351 | | 0.5199 | 17.24 | 1000 | 0.6019 | 0.5054 | | 0.2249 | 25.86 | 1500 | 0.6036 | 0.457... | 02b590ae5a3a3efa10ec70a4af7df519 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-mrpc-custom-tokenizer This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.2288 | 312c1ac0136433402d81670abe1ae734 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.7801 | 1.09 | 500 | 7.2517 | | 6.7962 | 2.18 | 1000 | 7.1073 | | 6.7132 | 3.27 | 1500 | 7.2439 | | 6.6765 | 4.36 | 2000 | 7.3869 ... | 7110b74e3c80ce84d998c29fd8cf271d |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr 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: 0.2978 - F1: 0.8326 | a8ac9ae0888c62b12ccbc7bf70d8e8e7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.574 | 1.0 | 191 | 0.3495 | 0.7889 | | 0.2649 | 2.0 | 382 | 0.2994 | 0.8242 | | 0.1716 | 3.0 | 573 | 0.2978 | 0.8326 | ... | 63cf655266b7176f16166d3d2503d0db |
apache-2.0 | ['translation'] | false | opus-mt-niu-en * source languages: niu * target languages: en * OPUS readme: [niu-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/niu-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](http... | df9710069addc663f370146c185c73c7 |
mit | ['spacy', 'token-classification'] | false | nb_core_news_sm Norwegian (Bokmål) pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler. | Feature | Description | | --- | --- | | **Name** | `nb_core_news_sm` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pi... | 65964afcf0c0a8f77f47bfce39095b74 |
mit | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.81 | | `TOKEN_P` | 99.71 | | `TOKEN_R` | 99.53 | | `TOKEN_F` | 99.62 | | `POS_ACC` | 96.74 | | `MORPH_ACC` | 95.32 | | `MORPH_MICRO_P` | 97.02 | | `MORPH_MICRO_R` | 96.07 | | `MORPH_MICRO_F` | 96.54 | | `SENTS_P` | 91.96 | | `SENTS_R` | 93.48 | | `SENTS_F` | ... | 61dfe10138ed69ca9d7db8606efb75c6 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | LouFerrignoHerculesBW2 Dreambooth model trained by bbugaev 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/fa... | 2ef879357e3b872a42451e32ea04f4de |
creativeml-openrail-m | ['text-to-image'] | false | aishwarya-inpaint-1 Dreambooth model trained by nileshpp with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/... | 4a7704bb65a57953e36cb7d899bc42ec |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 10.0 - label_smoothing_f... | 9d04757fb37666df424cf0013cab2f14 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2787 | 0.49 | 100 | 1.1127 | 0.4866 | | 1.089 | 0.98 | 200 | 0.9668 | 0.7139 | | 0.9134 | 1.47 | 300 | 0.8720 | 0.... | 751885428c47ccbe09b6a59d175fda69 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-wikitext-target-rotten_tomatoes This model is a fine-tuned version of [muhtasham/tiny-mlm-wikitext](https://huggingface.co/muhtasham/tiny-mlm-wikitext) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8868 - Accuracy: 0.7533 - F1: 0.7528 | 5bb5c4c68d6c231eed9995bb0d079696 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.6236 | 1.87 | 500 | 0.5413 | 0.7289 | 0.7285 | | 0.4892 | 3.75 | 1000 | 0.5137 | 0.7448 | 0.7435 | | 0.4112 |... | 93a044f207a3c8a9a2bba7037cf8cb3a |
mit | [] | false | Hanfu anime style on Stable Diffusion This is the `<hanfu-anime-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook... | 5a27e9ecf3a1115eeb3b561109f5f43b |
mit | ['generated_from_trainer'] | false | roberta-large-unlabeled-gab-semeval2023-task10-45000sample This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8859 | aa588d4f768cd30d1e37ce158c3b5e20 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 735529761da86e618ddf8c3e32eabda2 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.1552 | 1.0 | 1407 | 1.9502 | | 1.9918 | 2.0 | 2814 | 1.8859 | | d042118407a8d54959b11f6fa362623e |
mit | [] | false | fractal-flame on Stable Diffusion This is the `<fractal-flame>` 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 ca... | b5fb366911fb1053a48a16160682624d |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | Iridescent Jellyfish **Iridescent Jellyfish** is a Dreambooth model for the `iridescent` jellyfish concept (represented by the `ðŁĴŁ` identifier). It applies to the *animal* theme. It is fine-tuned from `runwayml/stable-diffusion-v1-5` checkpoint on a small dataset of jellyfish images. It can be used by modifying the... | 0bbbd9f6a11c8c555f0c477ccaf66962 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | Output Examples <table> <tr> <td>a oil painting of a <b>ðŁĴŁ</b> jellyfish</td> <td>a photo of a <b>ðŁĴŁ</b> jellyfish next to a dog</td> <td>a photo of a <b>ðŁĴŁ</b> jellyfish in the snow</td> </tr> <tr> <td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve... | e2c878a18f04e980f264118a071a2bab |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | Usage ```python from diffusers import StableDiffusionPipeline import torch device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') pipeline = StableDiffusionPipeline.from_pretrained('simonschoe/iridescent-jellyfish').to(device) prompt = "a photo of a ðŁĴŁ jellyfish in the snow" image = pipeline( ... | 6f636b28fb25fad01c06650fa35b56d0 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-ft-cv3-v3 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the "mozilla-foundation/common_voice_3_0 english" dataset: "train" and "validation" splits are used for training while "test" split is used for validation. It achieves the following... | f3a9e099b34d47d92793854700b25854 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 - mixed_precision_training: Native AMP | c39f1ae4907ec9e6c500d3117dd89ba8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5935 | 0.1 | 500 | 3.0085 | 1.0 | | 1.6296 | 0.21 | 1000 | 1.0879 | 0.5895 | | 0.7154 | 0.31 | 1500 | 0.8224 | 0.483... | 044424cfe9f1d4454a2d015ec36a8142 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.