license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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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 Transformers*: [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084), in particular: [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2).
| 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.CL)
1. [Computer Vision and Pattern Recognition](http://arxiv.org/rss/cs.CV)
1. [Information Retrieval](http://arxiv.org/rss/cs.IR)
1. [Machine Learning (cs)](http://arxiv.org/rss/cs.LG)
1. [Machine Learning (stat)](http://arxiv.org/rss/stat.ML)
| 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_scheduler_warmup_steps: 1000 - num_epochs: 50 - mixed_precision_training: Native AMP | 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 | | No log | 4.96 | 80 | 8.4694 | | No log | 5.96 | 96 | 8.2122 | | No log | 6.96 | 112 | 8.0040 | | No log | 7.96 | 128 | 7.8029 | | No log | 8.96 | 144 | 7.5950 | | No log | 9.96 | 160 | 7.4081 | | No log | 10.96 | 176 | 7.2391 | | No log | 11.96 | 192 | 7.0784 | | No log | 12.96 | 208 | 6.9139 | | No log | 13.96 | 224 | 6.7530 | | No log | 14.96 | 240 | 6.5983 | | No log | 15.96 | 256 | 6.4403 | | No log | 16.96 | 272 | 6.3025 | | No log | 17.96 | 288 | 6.1562 | | No log | 18.96 | 304 | 6.0147 | | No log | 19.96 | 320 | 5.8919 | | No log | 20.96 | 336 | 5.7709 | | No log | 21.96 | 352 | 5.6666 | | No log | 22.96 | 368 | 5.5818 | | No log | 23.96 | 384 | 5.5051 | | No log | 24.96 | 400 | 5.4356 | | No log | 25.96 | 416 | 5.3788 | | No log | 26.96 | 432 | 5.3230 | | No log | 27.96 | 448 | 5.2823 | | No log | 28.96 | 464 | 5.2513 | | No log | 29.96 | 480 | 5.2218 | | No log | 30.96 | 496 | 5.1910 | | No log | 31.96 | 512 | 5.1609 | | No log | 32.96 | 528 | 5.1500 | | No log | 33.96 | 544 | 5.1268 | | No log | 34.96 | 560 | 5.1012 | | No log | 35.96 | 576 | 5.0973 | | No log | 36.96 | 592 | 5.0769 | | No log | 37.96 | 608 | 5.0653 | | No log | 38.96 | 624 | 5.0489 | | No log | 39.96 | 640 | 5.0458 | | No log | 40.96 | 656 | 5.0379 | | No log | 41.96 | 672 | 5.0347 | | No log | 42.96 | 688 | 5.0161 | | No log | 43.96 | 704 | 5.0226 | | No log | 44.96 | 720 | 5.0215 | | No log | 45.96 | 736 | 5.0190 | | No log | 46.96 | 752 | 5.0087 | | No log | 47.96 | 768 | 5.0309 | | No log | 48.96 | 784 | 5.0232 | | No log | 49.96 | 800 | 5.0319 | | 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_scheduler_warmup_steps: 800 - num_epochs: 25 - mixed_precision_training: Native AMP | 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 | 0.8225 | | 0.9065 | 1.27 | 2000 | 0.5392 | 0.6244 | | 0.7891 | 1.59 | 2500 | 0.3554 | 0.4551 | | 0.7118 | 1.91 | 3000 | 0.3682 | 0.4608 | | 0.6061 | 2.23 | 3500 | 0.3384 | 0.4416 | | 0.5536 | 2.54 | 4000 | 0.2987 | 0.4042 | | 0.547 | 2.86 | 4500 | 0.2892 | 0.3892 | | 0.4841 | 3.18 | 5000 | 0.2890 | 0.3690 | | 0.4434 | 3.5 | 5500 | 0.2605 | 0.3636 | | 0.4542 | 3.81 | 6000 | 0.2932 | 0.3773 | | 0.4171 | 4.13 | 6500 | 0.2768 | 0.3550 | | 0.3697 | 4.45 | 7000 | 0.2443 | 0.3382 | | 0.3776 | 4.77 | 7500 | 0.2572 | 0.3366 | | 0.3448 | 5.09 | 8000 | 0.2267 | 0.3006 | | 0.3285 | 5.4 | 8500 | 0.2377 | 0.3023 | | 0.3165 | 5.72 | 9000 | 0.2344 | 0.2888 | | 0.3194 | 6.04 | 9500 | 0.2228 | 0.2699 | | 0.2737 | 6.36 | 10000 | 0.2201 | 0.2754 | | 0.2986 | 6.68 | 10500 | 0.2413 | 0.2850 | | 0.2836 | 6.99 | 11000 | 0.2117 | 0.2629 | | 0.2467 | 7.31 | 11500 | 0.2408 | 0.2877 | | 0.2577 | 7.63 | 12000 | 0.2134 | 0.2448 | | 0.2503 | 7.95 | 12500 | 0.2260 | 0.2600 | | 0.2371 | 8.26 | 13000 | 0.2081 | 0.2379 | | 0.2303 | 8.58 | 13500 | 0.2322 | 0.2668 | | 0.213 | 8.9 | 14000 | 0.2339 | 0.2586 | | 0.2029 | 9.22 | 14500 | 0.2300 | 0.2704 | | 0.2146 | 9.54 | 15000 | 0.2321 | 0.2533 | | 0.2044 | 9.85 | 15500 | 0.2393 | 0.2685 | | 0.2008 | 10.17 | 16000 | 0.2193 | 0.2467 | | 0.182 | 10.49 | 16500 | 0.2323 | 0.2611 | | 0.2 | 10.81 | 17000 | 0.2188 | 0.2537 | | 0.1855 | 11.13 | 17500 | 0.2436 | 0.2523 | | 0.1745 | 11.44 | 18000 | 0.2351 | 0.2473 | | 0.1705 | 11.76 | 18500 | 0.2556 | 0.2663 | | 0.1745 | 12.08 | 19000 | 0.2189 | 0.2229 | | 0.1641 | 12.4 | 19500 | 0.2192 | 0.2342 | | 0.1546 | 12.71 | 20000 | 0.2432 | 0.2228 | | 0.1661 | 13.03 | 20500 | 0.2323 | 0.2242 | | 0.1436 | 13.35 | 21000 | 0.2554 | 0.2496 | | 0.1443 | 13.67 | 21500 | 0.2195 | 0.2026 | | 0.151 | 13.99 | 22000 | 0.2400 | 0.2201 | | 0.1333 | 14.3 | 22500 | 0.2181 | 0.2235 | | 0.137 | 14.62 | 23000 | 0.2400 | 0.2254 | | 0.1303 | 14.94 | 23500 | 0.2265 | 0.2088 | | 0.1386 | 15.26 | 24000 | 0.2330 | 0.2152 | | 0.1325 | 15.58 | 24500 | 0.2328 | 0.2127 | | 0.1227 | 15.89 | 25000 | 0.2375 | 0.2077 | | 0.1196 | 16.21 | 25500 | 0.2394 | 0.2144 | | 0.1197 | 16.53 | 26000 | 0.2591 | 0.2171 | | 0.1122 | 16.85 | 26500 | 0.2383 | 0.2066 | | 0.1093 | 17.16 | 27000 | 0.2254 | 0.2042 | | 0.105 | 17.48 | 27500 | 0.2330 | 0.2008 | | 0.0982 | 17.8 | 28000 | 0.2317 | 0.1902 | | 0.1072 | 18.12 | 28500 | 0.2332 | 0.1971 | | 0.1033 | 18.44 | 29000 | 0.2313 | 0.1923 | | 0.0982 | 18.75 | 29500 | 0.2344 | 0.1934 | | 0.103 | 19.07 | 30000 | 0.2295 | 0.1902 | | 0.0945 | 19.39 | 30500 | 0.2352 | 0.1976 | | 0.0892 | 19.71 | 31000 | 0.2414 | 0.1920 | | 0.1003 | 20.03 | 31500 | 0.2300 | 0.1879 | | 0.0861 | 20.34 | 32000 | 0.2215 | 0.1778 | | 0.0845 | 20.66 | 32500 | 0.2321 | 0.1866 | | 0.0858 | 20.98 | 33000 | 0.2311 | 0.1850 | | 0.0785 | 21.3 | 33500 | 0.2341 | 0.1874 | | 0.0786 | 21.61 | 34000 | 0.2322 | 0.1916 | | 0.0793 | 21.93 | 34500 | 0.2358 | 0.1846 | | 0.0772 | 22.25 | 35000 | 0.2234 | 0.1770 | | 0.0786 | 22.57 | 35500 | 0.2180 | 0.1758 | | 0.0747 | 22.89 | 36000 | 0.2269 | 0.1830 | | 0.0734 | 23.2 | 36500 | 0.2320 | 0.1860 | | 0.067 | 23.52 | 37000 | 0.2324 | 0.1797 | | 0.0733 | 23.84 | 37500 | 0.2324 | 0.1772 | | 0.0701 | 24.16 | 38000 | 0.2293 | 0.1737 | | 0.0691 | 24.48 | 38500 | 0.2303 | 0.1750 | | 0.0613 | 24.79 | 39000 | 0.2280 | 0.1725 | | 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 | 0.5812 | | 0.6026 | 4.0 | 624 | 0.7727 | 0.6029 | | 0.6026 | 5.0 | 780 | 0.8121 | 0.6065 | | 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 Shuxin Zheng and Guolin Ke and Di He and Yanming Shen and Tie{-}Yan Liu}, title = {Do Transformers Really Perform Bad for Graph Representation?}, journal = {CoRR}, volume = {abs/2106.05234}, year = {2021}, url = {https://arxiv.org/abs/2106.05234}, eprinttype = {arXiv}, eprint = {2106.05234}, timestamp = {Tue, 15 Jun 2021 16:35:15 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-05234.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` | 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 | 1 | | 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](https://arxiv.org/abs/2209.09368). This model is based on [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50), but with updated vocabulary and checkpoint: - Added an extra language token `myv_XX` and 19K new BPE tokens for the Erzya language; - Fine-tuned to translate to Erzya: first from Russian, then from all 11 languages. The following code can be used to run translation using the model: ```Python from transformers import MBartForConditionalGeneration, MBart50Tokenizer def fix_tokenizer(tokenizer): """ Add a new language token to the tokenizer vocabulary (this should be done each time after its initialization) """ old_len = len(tokenizer) - int('myv_XX' in tokenizer.added_tokens_encoder) tokenizer.lang_code_to_id['myv_XX'] = old_len-1 tokenizer.id_to_lang_code[old_len-1] = 'myv_XX' tokenizer.fairseq_tokens_to_ids["<mask>"] = len(tokenizer.sp_model) + len(tokenizer.lang_code_to_id) + tokenizer.fairseq_offset tokenizer.fairseq_tokens_to_ids.update(tokenizer.lang_code_to_id) tokenizer.fairseq_ids_to_tokens = {v: k for k, v in tokenizer.fairseq_tokens_to_ids.items()} if 'myv_XX' not in tokenizer._additional_special_tokens: tokenizer._additional_special_tokens.append('myv_XX') tokenizer.added_tokens_encoder = {} def translate(text, model, tokenizer, src='ru_RU', trg='myv_XX', max_length='auto', num_beams=3, repetition_penalty=5.0, train_mode=False, n_out=None, **kwargs): tokenizer.src_lang = src encoded = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024) if max_length == 'auto': max_length = int(32 + 1.5 * encoded.input_ids.shape[1]) if train_mode: model.train() else: model.eval() generated_tokens = model.generate( **encoded.to(model.device), forced_bos_token_id=tokenizer.lang_code_to_id[trg], max_length=max_length, num_beams=num_beams, repetition_penalty=repetition_penalty, num_return_sequences=n_out or 1, **kwargs ) out = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) if isinstance(text, str) and n_out is None: return out[0] return out mname = 'slone/mbart-large-51-mul-myv-v1' model = MBartForConditionalGeneration.from_pretrained(mname) tokenizer = MBart50Tokenizer.from_pretrained(mname) fix_tokenizer(tokenizer) print(translate('Привет, собака!', model, tokenizer, src='ru_RU', trg='myv_XX')) | 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: 0.0442 | 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 | 1.1729 | 0.0446 | 0.0497 | 0.0472 | | 1.0648 | 3.0 | 135 | 1.1505 | 0.0470 | 0.0414 | 0.0442 | | 1.0737 | 4.0 | 180 | 1.1564 | 0.0472 | 0.0464 | 0.0468 | | 1.0445 | 5.0 | 225 | 1.1971 | 0.0529 | 0.0575 | 0.0552 | | 1.0296 | 6.0 | 270 | 1.1723 | 0.0578 | 0.0727 | 0.0652 | | 1.026 | 7.0 | 315 | 1.2735 | 0.0621 | 0.0606 | 0.0614 | | 1.0216 | 8.0 | 360 | 1.2214 | 0.0666 | 0.0700 | 0.0683 | | 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     | 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") def generate(instruction, knowledge, dialog): if knowledge != '': knowledge = '[KNOWLEDGE] ' + knowledge dialog = ' EOS '.join(dialog) query = f"{instruction} [CONTEXT] {dialog} {knowledge}" input_ids = tokenizer(f"{query}", return_tensors="pt").input_ids outputs = model.generate(input_ids, max_length=128, min_length=8, top_p=0.9, do_sample=True) output = tokenizer.decode(outputs[0], skip_special_tokens=True) return output | 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}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 | 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 | 1.7820 | 5 | | 2.1085 | 1.7720 | 6 | | 2.0908 | 1.7689 | 7 | | 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.** Scientific Abstract Simplification rewrites hard-to-read scientific abstracts😵 into simpler yet relevant scientific stories😇. We hope our model can make scientific knowledge accessible for everyone🤗. Try it now with the Hosted inference API on the right. You can choose an existing example or paste in any (perhaps full-of-jargon) abstract. Remember to prepend the instruction to the abstract ("summarize, simplify, and contextualize: "; notice, there is a whitespace after the colon). Local use refers to Section [Usage]( | 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. They are not to blame, we human like stories. So why do not we "translate" arcane scientific abstracts into simpler yet relevant scientific stories? Some renowned journals have already taken accessibility into consideration. For example, PNAS asks authors to submit Significance Statements targeting "an undergraduate-educated scientist." Science also includes an editor abstract for a quick dive. We therefore propose to *rewrite scientific abstracts into understandable scientific stories using AI*. To this end, we introduce a new corpus comprising PNAS abstract-significance pairs. We finetune an encoder-decoder Transformer model (a variant of Flan-T5) with the corpus. Our baseline model (SAS-baseline) shows promising capacity in simplifying and summarizing scientific abstracts. We hope our work can pave the last mile of scientific understanding and let people better enjoy the fruits of open science. As an ongoing effort, we are working on re-contextualizating abstracts for better storytelling and avoiding certain jargon tokens during inference time for better readability. <!-- We hypothesize the last mile of scientific understanding is cognitive. --> - **Model type:** Language model - **Developed by:** - PIs: Jason Clark and Hannah McKelvey, Montana State University - Fellow: Haining Wang, Indiana University Bloomington - Collaborator: Zuoyu Tian, Indiana University Bloomington - [LEADING](https://cci.drexel.edu/mrc/leading/) Montana State University Library, Project "TL;DR it": Automating Article Synopses for Search Engine Optimization and Citizen Science - **Language(s) (NLP):** English - **License:** MIT - **Parent Model:** [FLAN-T5-large](https://huggingface.co/google/flan-t5-large) | 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_baseline") model = AutoModelForSeq2SeqLM.from_pretrained("haining/sas_baseline") input_text = "The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making." encoding = tokenizer(INSTRUCTION + input_text, max_length=672, padding='max_length', truncation=True, return_tensors='pt') decoded_ids = model.generate(input_ids=encoding['input_ids'], attention_mask=encoding['attention_mask'], max_length=512, top_p=.9, do_sample=True) print(tokenizer.decode(decoded_ids[0], skip_special_tokens=True)) ``` | 6901b6ef1c3dda26e1eeb83387af714c |
mit | ['text2text generation'] | false | Test Tokens | Automated Readability Index (std.) | |----------------------------------|-----------------------------|-------------------|---------------------|---------------|------------------------------------| | Abstract | 3030/200/200 | 707,071 | 45,697 | 46,985 | 18.68 (2.85) | | Significance | 3030/200/200 | 375,433 | 24,901 | 24,426 | 17.89 (3.05) | | 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 (24GB memory each) GPUs. We saved the checkpoint with the lowest validation loss for inference. We used the AdamW optimizer and a learning rate of 3e-5 with fully sharded data parallel strategy. The model (\~780M parameter) was trained on Nov. 20, 2022. Notice, the readability of the significance statements is generally lower than the abstracts', but not by a large margin. Our incoming SAS-full model will leverage more corpora for scientific (re)contextualization, summarization, and simplification. | 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 standard test sets and handles downloading, processing, and tokenization for you. - [BERTScore](https://huggingface.co/spaces/evaluate-metric/bertscore): BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference sentences by cosine similarity. It has been shown to correlate with human judgment on sentence-level and system-level evaluation. Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language generation tasks. - [ROUGLE](https://huggingface.co/spaces/evaluate-metric/rouge)-1/2/L: ROUGE is a set of metrics and a software package used for evaluating automatic summarization and machine translation software in natural language processing. The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation. - [METEOR](https://huggingface.co/spaces/evaluate-metric/meteor): METEOR, an automatic metric for machine translation evaluation that is based on a generalized concept of unigram matching between the machine-produced translation and human-produced reference translations. Unigrams can be matched based on their surface forms, stemmed forms, and meanings; furthermore, METEOR can be easily extended to include more advanced matching strategies. Once all generalized unigram matches between the two strings have been found, METEOR computes a score for this matching using a combination of unigram-precision, unigram-recall, and a measure of fragmentation that is designed to directly capture how well-ordered the matched words in the machine translation are in relation to the reference. - [SARI](https://huggingface.co/spaces/evaluate-metric/sari): SARI is a metric used for evaluating automatic text simplification systems. The metric compares the predicted simplified sentences against the reference and the source sentences. It explicitly measures the goodness of words that are added, deleted and kept by the system. Sari = (F1_add + F1_keep + P_del) / 3 where F1_add: n-gram F1 score for add operation F1_keep: n-gram F1 score for keep operation P_del: n-gram precision score for delete operation n = 4, as in the original paper. - [The Automated Readability Index (ARI)](https://www.readabilityformulas.com/automated-readability-index.php): ARI is a readability test designed to assess the understandability of a text. Like other popular readability formulas, the ARI formula outputs a number which approximates the grade level needed to comprehend the text. For example, if the ARI outputs the number 10, this equates to a high school student, ages 15-16 years old; a number 3 means students in 3rd grade (ages 8-9 yrs. old) should be able to comprehend the text. Implementations of SacreBLEU, BERT Score, ROUGLE, METEOR, and SARI are from Huggingface [`evaluate`](https://pypi.org/project/evaluate/) v.0.3.0. ARI is from [`py-readability-metrics`](https://pypi.org/project/py-readability-metrics/) v.1.4.5. | 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↑ | 18.43 | | BERT Score F1↑ | 89.31 | | ROUGLE-1↑ | 48.14 | | ROUGLE-2↑ | 22.96 | | ROUGLE-L↑ | 32.29 | | METEOR↑ | 39.04 | | SARI↑ | 46.68 | | ARI↓ | 17.27 | Note: 1. Some generated texts are too short (less than 100 words) to calcualte meaningful ARI. We therefore concatenated adjecent five texts and compute ARI for the 400 longer texts (instead of original 2,000 texts). 2. BERT score, ROUGE, and METEOR are multiplied by 100. | 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: 0.9941 | 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 train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:       | 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 using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 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 | 0.0027 | | 1.8729 | 4.35 | 800 | 0.5726 | 0.0031 | | 3.5417 | 5.43 | 1000 | 0.5371 | 0.0067 | | 0.9087 | 6.52 | 1200 | 0.3512 | 0.1119 | | 1.2224 | 7.61 | 1400 | 0.2739 | 0.1911 | | 0.7597 | 8.69 | 1600 | 0.2151 | 0.3016 | | 0.6981 | 9.78 | 1800 | 0.1736 | 0.3749 | | 0.4779 | 10.87 | 2000 | 0.1548 | 0.4166 | | 0.4397 | 11.96 | 2200 | 0.1377 | 0.4510 | | 0.4101 | 13.04 | 2400 | 0.1480 | 0.4197 | | 0.3323 | 14.13 | 2600 | 0.1396 | 0.4398 | | 0.2565 | 15.22 | 2800 | 0.1351 | 0.4523 | | 0.2108 | 16.3 | 3000 | 0.1341 | 0.4541 | | 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_scheduler_warmup_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP | 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 | 0.69 | | No log | 3.99 | 224 | 1.0285 | 0.72 | | No log | 4.99 | 280 | 0.8538 | 0.79 | | No log | 5.99 | 336 | 0.8015 | 0.74 | | No log | 6.99 | 392 | 0.6694 | 0.82 | | No log | 7.99 | 448 | 0.6779 | 0.79 | | 1.0811 | 8.99 | 504 | 0.6414 | 0.81 | | 1.0811 | 9.99 | 560 | 0.6443 | 0.82 | | 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 Logits Accuracy: 0.8021 - Validation Loss: 0.2653 - Validation End Logits Accuracy: 0.9474 - Validation Start Logits Accuracy: 0.9474 - Epoch: 0 | 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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.5065 | 0.8924 | 0.8021 | 0.2653 | 0.9474 | 0.9474 | 0 | | 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_scheduler_warmup_ratio: 0.1 - num_epochs: 4 | 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 | 0.6087 | | 1.1433 | 4.0 | 896 | 0.9705 | 0.6410 | | 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](https://object.pouta.csc.fi/OPUS-MT-models/fr-kqn/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-kqn/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-kqn/opus-2020-01-09.eval.txt) | 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: 0.9427 - Precision: 0.9301 - Recall: 0.9556 | 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 | 0.9544 | 0.9427 | 0.9664 | | 0.0482 | 3.0 | 18528 | 0.2419 | 0.9521 | 0.9388 | 0.9658 | | 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 | 2760 | 0.0890 | 0.7298 | 0.8368 | 0.7797 | 0.9686 | | 0.0405 | 3.0 | 4140 | 0.1029 | 0.7792 | 0.8356 | 0.8064 | 0.9715 | | 0.0226 | 4.0 | 5520 | 0.1196 | 0.7872 | 0.8292 | 0.8077 | 0.9722 | | 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 our cleaners won't have to rush to serve you because there is a line of customers waiting for them. | 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 | 606 | 2.5543 | 0.0483 | 0.1661 | | 3.0646 | 4.0 | 808 | 2.4735 | 0.0537 | 0.1751 | | 2.6866 | 5.0 | 1010 | 2.4120 | 0.0591 | 0.1855 | | 2.6866 | 6.0 | 1212 | 2.3663 | 0.0618 | 0.1906 | | 2.6866 | 7.0 | 1414 | 2.3324 | 0.0667 | 0.1993 | | 2.5034 | 8.0 | 1616 | 2.3098 | 0.0684 | 0.2023 | | 2.5034 | 9.0 | 1818 | 2.2969 | 0.0696 | 0.2042 | | 2.4271 | 10.0 | 2020 | 2.2914 | 0.0708 | 0.2054 | | 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': 0.41794871794871796} | {'f1': 0.3246788612052483} | | 2.0379 | 1.22 | 100 | 1.9233 | {'accuracy': 0.4846153846153846} | {'f1': 0.4386383497855148} | | 1.7287 | 1.83 | 150 | 1.7200 | {'accuracy': 0.5256410256410257} | {'f1': 0.4806042289317683} | | 1.4667 | 2.44 | 200 | 1.6021 | {'accuracy': 0.5692307692307692} | {'f1': 0.533374137436958} | | 1.3444 | 3.05 | 250 | 1.5410 | {'accuracy': 0.5333333333333333} | {'f1': 0.4846134797922835} | | 1.1334 | 3.66 | 300 | 1.4674 | {'accuracy': 0.5743589743589743} | {'f1': 0.5533432367508125} | | 1.007 | 4.27 | 350 | 1.4096 | {'accuracy': 0.5923076923076923} | {'f1': 0.5801847507206119} | | 0.897 | 4.88 | 400 | 1.3674 | {'accuracy': 0.6} | {'f1': 0.5903283954748092} | | 0.7326 | 5.49 | 450 | 1.3359 | {'accuracy': 0.5923076923076923} | {'f1': 0.5793036546532927} | | 0.7105 | 6.1 | 500 | 1.3259 | {'accuracy': 0.6153846153846154} | {'f1': 0.6064330281486513} | | 0.6164 | 6.71 | 550 | 1.3183 | {'accuracy': 0.6102564102564103} | {'f1': 0.6014695572651212} | | 0.5804 | 7.32 | 600 | 1.3103 | {'accuracy': 0.6025641025641025} | {'f1': 0.5965366941171513} | | 0.5313 | 7.93 | 650 | 1.3041 | {'accuracy': 0.6102564102564103} | {'f1': 0.5980148081337936} | | 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 | 0.3239 | | No log | 4.0 | 40 | 0.6999 | 0.2958 | | No log | 5.0 | 50 | 0.6998 | 0.2676 | | 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 | | No log | 2.0 | 70 | nan | 0.1835 | 0.0654 | 0.1525 | 0.1523 | 18.4918 | | No log | 3.0 | 105 | nan | 0.1835 | 0.0654 | 0.1525 | 0.1523 | 18.4918 | | No log | 4.0 | 140 | 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 sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 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&hl=en). | 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 target figurative form from another figurative form without parallel figurative-figurative sentence pairs. | 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_bos_token_id=fig_ids.item(), num_beams=5, max_length=60,) text = tokenizer.decode(outs[0, 2:].tolist(), skip_special_tokens=True, clean_up_tokenization_spaces=False) ``` | 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 = "Gyeongju, Republic of korea", } ``` | 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 Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io). A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach. | 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 conferma che i cinque deputati che siedono in parlamento voteranno la sfiducia al governo guidato da Mariano Rajoy. Pochi voti, ma significativi quelli della formazione politica di Aitor Esteban, che interverrà nel pomeriggio. Pur con dimensioni molto ridotte, il partito basco si è trovato a fare da ago della bilancia in aula. E il sostegno alla mozione presentata dai Socialisti potrebbe significare per il primo ministro non trovare quei 176 voti che gli servono per continuare a governare. \" Perché dovrei dimettermi io che per il momento ho la fiducia della Camera e quella che mi è stato data alle urne \", ha detto oggi Rajoy nel suo intervento in aula, mentre procedeva la discussione sulla mozione di sfiducia. Il voto dei baschi ora cambia le carte in tavola e fa crescere ulteriormente la pressione sul premier perché rassegni le sue dimissioni. La sfiducia al premier, o un'eventuale scelta di dimettersi, porterebbe alle estreme conseguenze lo scandalo per corruzione che ha investito il Partito popolare. Ma per ora sembra pensare a tutt'altro. \"Non ha intenzione di dimettersi - ha detto il segretario generale del Partito popolare , María Dolores de Cospedal - Non gioverebbe all'interesse generale o agli interessi del Pp\".") >>> [{"generated_text": "il nazionalista rajoy: 'voteremo la sfiducia'"}] ``` or loaded using autoclasses: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("it5/it5-base-headline-generation") model = AutoModelForSeq2SeqLM.from_pretrained("it5/it5-base-headline-generation") ``` If you use this model in your research, please cite our work as: ```bibtex @article{sarti-nissim-2022-it5, title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation}, author={Sarti, Gabriele and Nissim, Malvina}, journal={ArXiv preprint 2203.03759}, url={https://arxiv.org/abs/2203.03759}, year={2022}, month={mar} } ``` | 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 | 0.4576 | | 0.1573 | 34.48 | 2000 | 0.6680 | 0.4532 | | 0.1301 | 43.1 | 2500 | 0.6823 | 0.4396 | | 0.1108 | 51.72 | 3000 | 0.6630 | 0.4263 | | 0.0941 | 60.34 | 3500 | 0.6574 | 0.4226 | | 0.0797 | 68.97 | 4000 | 0.6796 | 0.4174 | | 0.0689 | 77.59 | 4500 | 0.6426 | 0.4088 | | 0.0612 | 86.21 | 5000 | 0.6615 | 0.3949 | | 0.0528 | 94.83 | 5500 | 0.6769 | 0.3906 | | 0.0515 | 103.45 | 6000 | 0.6073 | 0.3827 | | 0.0394 | 112.07 | 6500 | 0.7099 | 0.3767 | | 0.0352 | 120.69 | 7000 | 0.7082 | 0.3688 | | 0.0324 | 129.31 | 7500 | 0.6939 | 0.3699 | | 0.028 | 137.93 | 8000 | 0.7018 | 0.3641 | | 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 | | 6.6069 | 5.45 | 2500 | 7.2288 | | 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](https://object.pouta.csc.fi/OPUS-MT-models/niu-en/opus-2020-01-21.zip) * test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/niu-en/opus-2020-01-21.test.txt) * test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/niu-en/opus-2020-01-21.eval.txt) | 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 Pipeline** | `tok2vec`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` | | **Components** | `tok2vec`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [UD Norwegian Bokmaal v2.8](https://github.com/UniversalDependencies/UD_Norwegian-Bokmaal) (Øvrelid, Lilja; Jørgensen, Fredrik; Hohle, Petter)<br />[NorNE: Norwegian Named Entities (commit: bd311de5)](https://github.com/ltgoslo/norne) (Language Technology Group (University of Oslo)) | | **License** | `MIT` | | **Author** | [Explosion](https://explosion.ai) | | 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` | 92.71 | | `DEP_UAS` | 88.41 | | `DEP_LAS` | 85.16 | | `LEMMA_ACC` | 96.90 | | `TAG_ACC` | 96.74 | | `ENTS_P` | 76.06 | | `ENTS_R` | 74.35 | | `ENTS_F` | 75.19 | | 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/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: | 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/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: aishwarya goel (use that on your prompt)  | 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_factor: 0.1 | 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 | 0.7834 | | 0.8618 | 1.96 | 400 | 0.7726 | 0.7941 | | 0.686 | 2.45 | 500 | 0.7337 | 0.8209 | | 0.6333 | 2.94 | 600 | 0.7350 | 0.8235 | | 0.5765 | 3.43 | 700 | 0.7561 | 0.8235 | | 0.5502 | 3.92 | 800 | 0.7273 | 0.8476 | | 0.5049 | 4.41 | 900 | 0.8137 | 0.8102 | | 0.4695 | 4.9 | 1000 | 0.7581 | 0.8289 | | 0.4657 | 5.39 | 1100 | 0.8404 | 0.8048 | | 0.4549 | 5.88 | 1200 | 0.7800 | 0.8369 | | 0.4305 | 6.37 | 1300 | 0.8575 | 0.8235 | | 0.4209 | 6.86 | 1400 | 0.8572 | 0.8102 | | 0.3983 | 7.35 | 1500 | 0.8392 | 0.8316 | | 0.4139 | 7.84 | 1600 | 0.8152 | 0.8209 | | 0.393 | 8.33 | 1700 | 0.8261 | 0.8289 | | 0.3979 | 8.82 | 1800 | 0.8328 | 0.8235 | | 0.3928 | 9.31 | 1900 | 0.8364 | 0.8209 | | 0.3848 | 9.8 | 2000 | 0.8322 | 0.8235 | | 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 | 5.62 | 1500 | 0.5224 | 0.7636 | 0.7629 | | 0.3454 | 7.49 | 2000 | 0.5365 | 0.7627 | 0.7624 | | 0.2899 | 9.36 | 2500 | 0.5962 | 0.7655 | 0.7651 | | 0.2447 | 11.24 | 3000 | 0.6489 | 0.7561 | 0.7554 | | 0.2025 | 13.11 | 3500 | 0.6943 | 0.7692 | 0.7688 | | 0.1671 | 14.98 | 4000 | 0.7455 | 0.7627 | 0.7621 | | 0.1418 | 16.85 | 4500 | 0.7962 | 0.7608 | 0.7600 | | 0.1239 | 18.73 | 5000 | 0.8868 | 0.7533 | 0.7528 | | 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. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:     | 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 can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:                                   | 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 `instance_prompt`: **a photo of a ðŁĴŁ jellyfish in the snow** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! | 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/main/output/jelly%20(4).png" style="height:200px"> </td> <td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(5).png" style="height:200px"> </td> <td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(6).png" style="height:200px"> </td> </tr> <tr> <td>a photo of a <b>ðŁĴŁ</b> jellyfish on top of a mountain</td> <td>a photo of a <b>ðŁĴŁ</b> jellyfish in the sky</td> <td>a photo of a <b>ðŁĴŁ</b> jellyfish</td> </tr> <tr> <td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(7).png" style="height:200px"> </td> <td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(8).png" style="height:200px"> </td> <td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(9).png" style="height:200px"> </td> </tr> <tr> <td>a photo of a <b>ðŁĴŁ</b> jellyfish skydiving</td> <td>a photo of a <b>ðŁĴŁ</b> jellyfish sutfing on a surfboard</td> <td>a photo of a choclate <b>ðŁĴŁ</b> jellyfish</td> </tr> <tr> <td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(10).jpg" style="height:200px"> </td> <td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(11).jpg" style="height:200px"> </td> <td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(12).jpg" style="height:200px"> </td> </tr> <tr> <td>a photo of a <b>ðŁĴŁ</b> jellyfish shooting fireworks in the sky</td> <td>a photo of a <b>ðŁĴŁ</b> jellyfish on rollerblades</td> <td>a photo of a <b>ðŁĴŁ</b> jellyfish in a beer bottle</td> </tr> <tr> <td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(13).jpg" style="height:200px"> </td> <td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(14).jpg" style="height:200px"> </td> <td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(15).jpg" style="height:200px"> </td> </tr> <tr> <td>a colorful sketch of a <b>ðŁĴŁ</b> jellyfish</td> <td>a photo of a <b>ðŁĴŁ</b> jellyfish in the jungle</td> <td>a mystic <b>ðŁĴŁ</b> jellyfish, trending on artstation</td> </tr> <tr> <td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(1).png" style="height:200px"> </td> <td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(2).png" style="height:200px"> </td> <td align="center"><img src="https://huggingface.co/simonschoe/iridescent-jellyfish/resolve/main/output/jelly%20(3).png" style="height:200px"> </td> </tr> </table> | 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( prompt, num_inference_steps=50, guidance_scale=7, num_images_per_prompt=1 ).images[0] image ``` | 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 results on the evaluation set: - Loss: 0.5787 - Wer: 0.2470 | 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 | 0.4839 | | 0.6387 | 0.42 | 2000 | 0.7290 | 0.4302 | | 0.5322 | 0.52 | 2500 | 0.6864 | 0.4044 | | 0.497 | 0.63 | 3000 | 0.6294 | 0.3746 | | 0.4659 | 0.73 | 3500 | 0.6388 | 0.3745 | | 0.4452 | 0.84 | 4000 | 0.6122 | 0.3570 | | 0.4356 | 0.94 | 4500 | 0.5770 | 0.3443 | | 0.3976 | 1.05 | 5000 | 0.6145 | 0.3296 | | 0.3767 | 1.15 | 5500 | 0.6099 | 0.3325 | | 0.3704 | 1.25 | 6000 | 0.5998 | 0.3263 | | 0.3541 | 1.36 | 6500 | 0.6070 | 0.3250 | | 0.3592 | 1.46 | 7000 | 0.6076 | 0.3352 | | 0.3508 | 1.57 | 7500 | 0.5712 | 0.3239 | | 0.3437 | 1.67 | 8000 | 0.5729 | 0.3202 | | 0.352 | 1.78 | 8500 | 0.5465 | 0.3100 | | 0.34 | 1.88 | 9000 | 0.5418 | 0.3059 | | 0.4086 | 1.99 | 9500 | 0.5189 | 0.3053 | | 0.2968 | 2.09 | 10000 | 0.5373 | 0.3076 | | 0.2968 | 2.2 | 10500 | 0.5602 | 0.3061 | | 0.2956 | 2.3 | 11000 | 0.5651 | 0.3051 | | 0.2863 | 2.41 | 11500 | 0.5476 | 0.2982 | | 0.2852 | 2.51 | 12000 | 0.5579 | 0.2954 | | 0.292 | 2.61 | 12500 | 0.5451 | 0.2953 | | 0.2877 | 2.72 | 13000 | 0.5468 | 0.2905 | | 0.285 | 2.82 | 13500 | 0.5283 | 0.2908 | | 0.2872 | 2.93 | 14000 | 0.5240 | 0.2867 | | 0.3286 | 3.03 | 14500 | 0.5078 | 0.2846 | | 0.2526 | 3.14 | 15000 | 0.5373 | 0.2836 | | 0.2494 | 3.24 | 15500 | 0.5566 | 0.2861 | | 0.2534 | 3.35 | 16000 | 0.5378 | 0.2859 | | 0.2435 | 3.45 | 16500 | 0.5225 | 0.2813 | | 0.3144 | 3.56 | 17000 | 0.5203 | 0.2808 | | 0.2501 | 3.66 | 17500 | 0.5176 | 0.2785 | | 0.2469 | 3.76 | 18000 | 0.5022 | 0.2795 | | 0.242 | 3.87 | 18500 | 0.5228 | 0.2757 | | 0.242 | 3.97 | 19000 | 0.5024 | 0.2788 | | 0.2205 | 4.08 | 19500 | 0.5318 | 0.2729 | | 0.2149 | 4.18 | 20000 | 0.5492 | 0.2763 | | 0.2186 | 4.29 | 20500 | 0.5599 | 0.2769 | | 0.2191 | 4.39 | 21000 | 0.5493 | 0.2695 | | 0.218 | 4.5 | 21500 | 0.5385 | 0.2709 | | 0.2046 | 4.6 | 22000 | 0.5326 | 0.2718 | | 0.2064 | 4.71 | 22500 | 0.5591 | 0.2725 | | 0.2066 | 4.81 | 23000 | 0.5283 | 0.2700 | | 0.2102 | 4.92 | 23500 | 0.5456 | 0.2713 | | 0.3345 | 5.02 | 24000 | 0.5474 | 0.2698 | | 0.1891 | 5.12 | 24500 | 0.5466 | 0.2672 | | 0.1954 | 5.23 | 25000 | 0.5691 | 0.2731 | | 0.1971 | 5.33 | 25500 | 0.5595 | 0.2741 | | 0.1995 | 5.44 | 26000 | 0.5609 | 0.2716 | | 0.1911 | 5.54 | 26500 | 0.5513 | 0.2684 | | 0.1954 | 5.65 | 27000 | 0.5282 | 0.2683 | | 0.193 | 5.75 | 27500 | 0.5460 | 0.2644 | | 0.1974 | 5.86 | 28000 | 0.5415 | 0.2650 | | 0.1947 | 5.96 | 28500 | 0.5227 | 0.2656 | | 0.1836 | 6.07 | 29000 | 0.5361 | 0.2743 | | 0.1741 | 6.17 | 29500 | 0.5637 | 0.2649 | | 0.1776 | 6.27 | 30000 | 0.5705 | 0.2680 | | 0.1747 | 6.38 | 30500 | 0.5587 | 0.2667 | | 0.1761 | 6.48 | 31000 | 0.5480 | 0.2683 | | 0.1715 | 6.59 | 31500 | 0.5547 | 0.2627 | | 0.2424 | 6.69 | 32000 | 0.5254 | 0.2610 | | 0.1756 | 6.8 | 32500 | 0.5301 | 0.2633 | | 0.1761 | 6.9 | 33000 | 0.5267 | 0.2658 | | 0.1751 | 7.01 | 33500 | 0.5611 | 0.2677 | | 0.1653 | 7.11 | 34000 | 0.5617 | 0.2663 | | 0.1591 | 7.22 | 34500 | 0.5435 | 0.2642 | | 0.1559 | 7.32 | 35000 | 0.5608 | 0.2611 | | 0.1604 | 7.43 | 35500 | 0.5477 | 0.2611 | | 0.162 | 7.53 | 36000 | 0.5257 | 0.2559 | | 0.1579 | 7.63 | 36500 | 0.5398 | 0.2570 | | 0.162 | 7.74 | 37000 | 0.5566 | 0.2605 | | 0.2351 | 7.84 | 37500 | 0.5371 | 0.2564 | | 0.1566 | 7.95 | 38000 | 0.5507 | 0.2565 | | 0.1515 | 8.05 | 38500 | 0.5640 | 0.2544 | | 0.1459 | 8.16 | 39000 | 0.5739 | 0.2523 | | 0.1463 | 8.26 | 39500 | 0.5596 | 0.2522 | | 0.1466 | 8.37 | 40000 | 0.5522 | 0.2537 | | 0.2372 | 8.47 | 40500 | 0.5567 | 0.2520 | | 0.1488 | 8.58 | 41000 | 0.5546 | 0.2506 | | 0.1492 | 8.68 | 41500 | 0.5533 | 0.2518 | | 0.1454 | 8.78 | 42000 | 0.5488 | 0.2508 | | 0.148 | 8.89 | 42500 | 0.5635 | 0.2526 | | 0.1424 | 8.99 | 43000 | 0.5513 | 0.2509 | | 0.1356 | 9.1 | 43500 | 0.5534 | 0.2527 | | 0.1346 | 9.2 | 44000 | 0.5735 | 0.2497 | | 0.1346 | 9.31 | 44500 | 0.5710 | 0.2489 | | 0.1401 | 9.41 | 45000 | 0.5561 | 0.2491 | | 0.2212 | 9.52 | 45500 | 0.5564 | 0.2482 | | 0.1369 | 9.62 | 46000 | 0.5658 | 0.2484 | | 0.1323 | 9.73 | 46500 | 0.5582 | 0.2495 | | 0.1369 | 9.83 | 47000 | 0.5560 | 0.2503 | | 0.1368 | 9.94 | 47500 | 0.5552 | 0.2489 | | 0.1333 | 10.04 | 48000 | 0.5953 | 0.2491 | | 0.1305 | 10.14 | 48500 | 0.5818 | 0.2520 | | 0.1316 | 10.25 | 49000 | 0.5773 | 0.2506 | | 0.1334 | 10.35 | 49500 | 0.5882 | 0.2485 | | 0.1351 | 10.46 | 50000 | 0.5750 | 0.2483 | | 0.1337 | 10.56 | 50500 | 0.5910 | 0.2486 | | 0.2241 | 10.67 | 51000 | 0.5732 | 0.2491 | | 0.1327 | 10.77 | 51500 | 0.5839 | 0.2493 | | 0.1364 | 10.88 | 52000 | 0.5724 | 0.2464 | | 0.1305 | 10.98 | 52500 | 0.5758 | 0.2468 | | 0.128 | 11.09 | 53000 | 0.5811 | 0.2482 | | 0.1267 | 11.19 | 53500 | 0.5903 | 0.2483 | | 0.1262 | 11.29 | 54000 | 0.5792 | 0.2483 | | 0.1291 | 11.4 | 54500 | 0.5735 | 0.2497 | | 0.1228 | 11.5 | 55000 | 0.5920 | 0.2494 | | 0.1249 | 11.61 | 55500 | 0.5907 | 0.2488 | | 0.1266 | 11.71 | 56000 | 0.5786 | 0.2486 | | 0.1235 | 11.82 | 56500 | 0.5790 | 0.2473 | | 0.1243 | 11.92 | 57000 | 0.5787 | 0.2470 | | 044424cfe9f1d4454a2d015ec36a8142 |
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