license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | bel-deu | tatoeba-test-v2021-08-07 | 0.63720 | 44.8 | 551 | 4182 | | rus-deu | tatoeba-test-v2021-08-07 | 0.69768 | 51.8 | 12800 | 98842 | | ukr-deu | tatoeba-test-v2021-08-07 | 0.70860 | 54.7 | 10319 | 64646 | | bel-deu | flores101-devtest | 0.47052 | 1... | b1b507a3ecfd25eb387732527f135d5b |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-en-to-it-b32 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the ccmatrix dataset. It achieves the following results on the evaluation set: - Loss: 2.1496 - Bleu: 9.6816 - Gen Len: 56.5347 | a0569e5441f9de0c3975a9b3eeca7e54 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:| | No log | 1.0 | 282 | 2.9409 | 2.6764 | 69.2487 | | 3.3809 | 2.0 | 564 | 2.8277 | 2.4974 | 87.428 | | 3.3809 |... | 99ca8bbf3ba53b1e689ecf480e881ccd |
apache-2.0 | ['generated_from_trainer'] | false | HateXplain-All-agreed-labeled This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7322 - Accuracy: 0.8711 | 2754bd6c73d0c4c1eed06d19689b9f28 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-qqp-custom-tokenizer-expand-vocab This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8901 | 533a50adab503baa9d30c3f756258d34 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.9945 | 0.4 | 500 | 4.2995 | | 4.253 | 0.8 | 1000 | 3.9195 | | 3.8857 | 1.2 | 1500 | 3.6343 | | 3.6372 | 1.6 | 2000 | 3.4816 ... | 1904bded2b2262b66465306dfc04d77c |
other | ['generated_from_keras_callback'] | false | sayakpaul/mit-b0-finetuned-sidewalks-dummy This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8504 - Validation Loss: 0.6735 - Validation Mean Iou: 0.2144 - Validation Mean Accuracy... | 9470e1ea4d8832a86be142518edd43b5 |
other | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Validation Mean Iou | Validation Mean Accuracy | Validation Overall Accuracy | Validation Accuracy Unlabeled | Validation Accuracy Flat-road | Validation Accuracy Flat-sidewalk | Validation Accuracy Flat-crosswalk | Validation Accuracy Flat-cyclinglane | Validation Ac... | 1279376528c74d924f4ff54e5a574647 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_token_2e-05_all_16_02_2022-15_43_42 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1750 - Precision: 0.3286... | 9b8ff1b78e3ab512a62d6ad24dfbd44a |
afl-3.0 | [] | false | XLM-RoBERTa-Urdu-Classification This [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) text classification model trained on Urdu sentiment [data-set](https://huggingface.co/datasets/hassan4830/urdu-binary-classification-data) performs binary sentiment classification on any given Urdu sentence. The model has... | ec9f43674fdaac4c5b8eda7064a6b654 |
afl-3.0 | [] | false | Model description XLM-RoBERTa is a scaled cross-lingual sentence encoder. It is trained on 2.5T of data across 100 languages data filtered from Common Crawl. XLM-R achieves state-of-the-arts results on multiple cross-lingual benchmarks. The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation ... | 367acc796e8937465fd4fed30eb00aa0 |
afl-3.0 | [] | false | How to use You can import this model directly from the transformers library: ```python >>> from transformers import AutoTokenizer, AutoModelForSequenceClassification >>> tokenizer = AutoTokenizer.from_pretrained("hassan4830/xlm-roberta-base-finetuned-urdu") >>> model = AutoModelForSequenceClassification.from_pretrai... | 786f9760bd0d59c6e213d183c3de2e76 |
mit | ['generated_from_trainer'] | false | roberta_checkpoint-finetuned-squad This model is a fine-tuned version of [WillHeld/roberta-base-coqa](https://huggingface.co/WillHeld/roberta-base-coqa) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 0.8934 | 81106f2a9b95ece3308c46390ba29587 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.8468 | 1.0 | 5536 | 0.8168 | | 0.6239 | 2.0 | 11072 | 0.8237 | | 0.4805 | 3.0 | 16608 | 0.8934 | | 37f0b816de46f254b1d566db235b37ca |
apache-2.0 | [] | false | distilbert-base-en-fr-zh-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original ac... | 18f440a591754994782d6b73dc0f8e56 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-fr-zh-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-fr-zh-cased") ``` To generate other smaller versions of multilingual transformers please visit [... | 042471834f0f1789ce615dd227b882a6 |
mit | [] | false | arcane-cyberpunk-random on Stable Diffusion This is the `<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... | 42d28629b059b4fc2e7c0c08f237be9f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_wnli This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3447 - Accuracy: 0.5634 | 190cfdbc901d8e6be1f548823326f9ad |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.552 | 1.0 | 3 | 0.3618 | 0.4366 | | 0.3998 | 2.0 | 6 | 0.3864 | 0.5634 | | 0.3807 | 3.0 | 9 | 0.4036 | 0.... | b3a09813b220eab035cca6901b8a6f22 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_output This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9385 - Accuracy: 0.3333 - F1: 0.5 - Precision: 0.3333 - Recall: 1.0 | 0f35638bb22ab7f6868db05897ca95c4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.669 | 1.0 | 12 | 0.6793 | 0.5 | 0.6667 | 0.5 | 1.0 | | 0.5872 | 2.0 |... | 0417eada47d811221ce62b8d5bef1f04 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5503 | 19565984db33b7cc0b20fa424947b19d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6214 | 1.0 | 291 | 2.2471 | | 2.0594 | 2.0 | 582 | 1.9293 | | 1.8563 | 3.0 | 873 | 1.7961 | | 1.7442 | 4.0 | 1164 | 1.7518 ... | 4d38c7f47214af4dab3073e2387206c4 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-XL-NL2 (Deep-Narrow version) T5-Efficient-XL-NL2 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was ... | 0c91d9d8a18e0cd1a1d0511b7d5df582 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-xl-nl2** - is of model type **Xl** with the following variations: - **nl** is **2** It has **267.8** million parameters and thus requires *ca.* **1071.2 MB** of memory in full precision (*fp32*) or **535.6 MB** of memory in half precision (*fp16* or... | 1751d6101ef0224a27ab2fa20fc149e0 |
mit | ['generated_from_trainer'] | false | camembert-base-cae-composante This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2323 - Precision: 0.9589 - Recall: 0.9579 - F1: 0.9582 | 2963e10378efdb7d30da02fef0fbbac6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 0.8329 | 1.0 | 309 | 0.2321 | 0.9459 | 0.9450 | 0.9451 | | 0.2118 | 2.0 | 618 | 0.2280 | 0.9447 ... | 75a1aa01364825b5f4f8fa6fb5799a39 |
mit | ['generated_from_trainer'] | false | keen_jackson This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000... | 966faf3dc883069d1fc4f0a2cfd6511f |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tom... | 6f8b5a2fecc288d7a49167838d17e5db |
mit | ['Instagram', 'NER', 'Named Entity Recognition', 'Food Entity Extraction', 'Social Media', 'Informal text'] | false | Model description **InstaFoodBERT-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** of Food entities on informal text (social media like). It has been trained to recognize a single entity: food (FOOD). Specifically, this model is a *bert-base-cased* model that was fine-tuned on a... | 1c844482d8b03a2d4b1007063a8bdc51 |
mit | ['Instagram', 'NER', 'Named Entity Recognition', 'Food Entity Extraction', 'Social Media', 'Informal text'] | false | How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Dizex/InstaFoodBERT-NER") model = AutoModelForTokenClassification.from_pretrained("Di... | e51308c5fd1c12b1968a5f3eeb5d1a6b |
mit | ['text-to-image'] | false | Mann-E 3 revision 3 "Prompt Muse" __Mann-E__ is a _text to image_ model which has been developed by [Muhammadreza Haghiri](https://haghiri75.com/en) in order to be part of the [Cognitive Web](https://opencognitives.com) movement and projects. This is revision 3 of the model and it's the first one to have a code name.... | 93bcaf1604ea7f122e9025cc48c234cd |
mit | ['text-to-image'] | false | Code The following code is written for _CUDA_ supported devices. If you use UI's or inference tools on other devices, you may need to tweak them in order to get them to the work. Otherwise, it will be fine. First, you need to install required libraries: ``` pip3 install diffusers transformers scipy ftfy accelerate ... | c7760cb2594b2cdf05dcd50458fcecd2 |
apache-2.0 | ['exbert'] | false | BatteryOnlyBERT-cased model Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective. It was introduced in [this paper](paper_link) and first released in [this repository](https://github.com/ShuHuang/batterybert). This model is case-sensitive: it makes a differenc... | d0214bb413a05fc290c19592de17c30f |
apache-2.0 | ['exbert'] | false | Model description BatteryOnlyBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic proce... | 873a6a11bc083f3295e9e289cc12207f |
apache-2.0 | ['exbert'] | false | Training data The BatteryOnlyBERT model was pretrained on the full text of battery papers only. The paper corpus contains 1.87B tokens form a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DO... | bf96c88e5a02f41fb9819a8e7118eb2c |
apache-2.0 | ['exbert'] | false | Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 28,996. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of th... | 345d1fc837542389a1889f696705ea25 |
apache-2.0 | ['exbert'] | false | Pretraining The model was trained on 8 NVIDIA DGX A100 GPUs for 1,500,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 ... | 26470fc70d52d195938bb39ecfdf3bde |
apache-2.0 | ['exbert'] | false | Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=batterybert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aim... | 146f1b64411af4c2fd0dd96c496e4bf2 |
apache-2.0 | ['exbert'] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='batterydata/batteryonlybert-cased') >>> unmasker("Hello I'm a <mask> model.") ``` Here is how to use this model to get the features of ... | c4fa23be284c0b29d033392eaa2f64dc |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Craig-Wazowski-style Dreambooth model trained by Kagerage 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/fas... | c6eb0453c8f64026e4e33d214aafe4b5 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ft1500_reg1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6165 - Mse: 0.6165 - Mae: 0.6069 - R2: 0.4197 - Accuracy: 0.5007 | fb9fa9dc915a21a388932d80d2f7bfe9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:| | 0.7297 | 1.0 | 3000 | 0.9128 | 0.9128 | 0.7501 | 0.1408 | 0.4113 | | 0.4692 | 2.0 | 6000 | 0... | 5f75ec78d72abe873b099a0802b2cd66 |
apache-2.0 | ['generated_from_trainer'] | false | t5-response-gen This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9994 - Rouge1: 19.7051 - Rouge2: 6.4371 - Rougel: 16.1965 - Rougelsum: 18.3535 - Gen Len: 18.94 | 13f6f8588fd069cef975533643836a52 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.6936 | 0.2 | 250 | 2.3362 | 17.8551 | 5.8099 | 15.0919 | 16.3812 | 19.0 ... | bb6e60845bfec9c5a1cef26d7988a9a5 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | average_word_embeddings_glove.840B.300d This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 9101de5b4090117f5d06ad5d18a30fcc |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | 0aa31fff8d465fc4bd2d453181dc9f3a |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/average_word_embeddings_glove.840B.300d) | ddd2d0a8879389f757590ac965c5c1ce |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Full Model Architecture ``` SentenceTransformer( (0): WordEmbeddings( (emb_layer): Embedding(2196018, 300) ) (1): Pooling({'word_embedding_dimension': 300, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` | 8a1d9958c62ab27f9cb086813d635527 |
gpl-3.0 | ['electra', 'tagalog', 'filipino'] | false | ELECTRA Tagalog Base Uncased Generator Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community. This is the generator model used to sample synthetic text and pretr... | 0b543b94c79f3dd804cea6a4477856ab |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large V2 Hindi - Drishti Sharma This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2093 - Wer: 10.0517 | fd10ba2627ac41323f52796dacdb8847 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - 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: 100 - training_steps: 3000 - mixed_precisio... | c059d425773eb82a39dafeb16e1af59a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0056 | 3.67 | 3000 | 0.2093 | 10.0517 | | e408aaf193beac38c94f91f637581537 |
apache-2.0 | ['translation'] | false | opus-mt-xh-es * source languages: xh * target languages: es * OPUS readme: [xh-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/xh-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 56cb640e36311028c2850c3bdb5e31de |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_sst2_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4333 - Accuracy: 0.8016 | 9d9f788e31366212904f8ae84e956648 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4969 | 1.0 | 527 | 0.4333 | 0.8016 | | 0.2781 | 2.0 | 1054 | 0.4999 | 0.7833 | | 0.2274 | 3.0 | 1581 | 0.4782 | 0.... | 380abb49c236b975eef5111dd5e3dfa9 |
apache-2.0 | ['text2text-generation'] | false | Running the model on a CPU <details> <summary> Click to expand </summary> ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base") input_text = "translate Engl... | 3b8f22164661a897c848da6b597e6b4a |
apache-2.0 | ['text2text-generation'] | false | pip install accelerate from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base", device_map="auto") input_text = "translate English to German: How old are you?" input_ids = ... | 7035c9caeff0fec909319908df21d52a |
apache-2.0 | ['text2text-generation'] | false | pip install accelerate import torch from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base", device_map="auto", torch_dtype=torch.float16) input_text = "translate English t... | 4cee4355907b34dff1ad5679812bf05e |
apache-2.0 | ['text2text-generation'] | false | pip install bitsandbytes accelerate from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base", device_map="auto", load_in_8bit=True) input_text = "translate English to German... | 82974e8e94d011a3ba33e14518597d37 |
apache-2.0 | ['text2text-generation'] | false | Model Recycling [Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=9.16&mnli_lp=nan&20_newsgroup=3.34&ag_news=1.49&amazon_reviews_multi=0.21&anli=13.91&boolq=16.75&cb=23.12&cola=9.97&copa=34.50&dbpedia=6.90&esnli=5.37&financial_phrasebank=18.66&imdb=0.33&isear=1.37&mnli=11.74&mrpc=... | e4b54f365e7bc25aa5e8f3f075d1ed21 |
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 the bionlp2004 dataset. It achieves the following results on the evaluation set: - Loss: 0.2098 - Precision: 0.7522 - Recall: 0.8140 - F1: 0.7819 - Accuracy: 0.9379 | 36c53efb0e38cbf7d01950b142e977b4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2255 | 1.0 | 2078 | 0.2073 | 0.7080 | 0.7877 | 0.7457 | 0.9305 | | 0.1709 | 2.0 |... | 8566e09e03866c8f4dd4baf91478839f |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP | 0ed7f6c170cd8a1665a7c1d47ac273f1 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'ja', 'japanese'] | false | Japanese Stable Diffusion Pokemon Model Card <!--  --> Stable-Diffusion-Pokemon-ja is a Japanese-specific latent text-to-image diffusion model capable of generating Pokemon images given any text input. This model was trained by u... | ac7f002a6d4552315be5cec70dd63063 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'ja', 'japanese'] | false | Model Details - **Developed by:** Zhipeng Yang - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** Japanese - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/... | 3ea7dfcc9469fe1e4a985851b294be06 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'ja', 'japanese'] | false | Examples Firstly, install our package as follows. This package is modified [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Japanese Stable Diffusion. ```bash pip install git+https://github.com/rinnakk/japanese-stable-diffusion sudo apt-get install git-lfs git clone https://huggingface.co/s... | 85153d4c652b1ea8517bb4316aecbd1d |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'ja', 'japanese'] | false | git clone https://huggingface.co/svjack/Stable-Diffusion-Pokemon-ja pretrained_model_name_or_path = "Stable-Diffusion-Pokemon-ja" pipe = JapaneseStableDiffusionPipeline.from_pretrained(pretrained_model_name_or_path, scheduler=scheduler, use_auth_token=True) ... | 506d86de2f2e748753fe4e6f1507be3d |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'ja', 'japanese'] | false | disable safety_checker pipe.safety_checker = lambda images, clip_input: (images, False) imgs = pipe("鉢植えの植物を頭に載せた漫画のキャラクター", num_inference_steps = 100 ) image = imgs.images[0] image.save("output.png") ``` | 4837c5466661565a60faf6bca9b8453a |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'ja', 'japanese'] | false | Generator Results comparison [https://github.com/svjack/Stable-Diffusion-Pokemon](https://github.com/svjack/Stable-Diffusion-Pokemon)  . Feel free to try Example 1, and modify it to inspect model ability. | 96362b7c9a6b7d298c742c2cdb53dd0a |
apache-2.0 | ['simplification'] | false | Model Loading The model can be loaded in the following way: ``` from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("philippelaban/keep_it_simple") kis_model = AutoModelForCausalLM.from_pretrained("philippelaban/keep_it_simple") ``` | 30732c2366bdbc759cf9cd227620874d |
apache-2.0 | ['simplification'] | false | Example use And then used by first inputting a paragraph for simplification, followed by a `bos_token` to indicate to the model to start simplifying. Imagine we want to simplify the following paragraph: ``` A small capsule containing asteroid soil samples that was dropped from 136,700 miles in space by Japan's Hayabu... | 470b5b4e2525e13db679c35f8f6ed3f2 |
apache-2.0 | ['simplification'] | false | Example output When run, an output similar to the following should be obtained: A small capsule containing samples of asteroid soil that was dropped from 136,700 miles, Japan's Hayabusa2 space probe, landed as planned on December 6. The mission was extremely precise, said many in Japan, and they took pride in its su... | 8134e727d97a997d3d6d87eede7bf005 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | fastbooth-jsjessy-800 Dreambooth model trained by eicu 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-s... | 72c852c4b4f258de8471cb6c08b0bfa3 |
apache-2.0 | ['automatic-speech-recognition'] | false | This model is trained on the PSST Challenge data, with a subset of TIMIT that was augmented using Room Impulse Response (RIR). A file containing the list of TIMIT IDs is in the repository (`timit-ids.txt`) The model was finetuned on [Wav2vec 2.0 Base, No finetuning](https://github.com/pytorch/fairseq/tree/main/exampl... | aace711db4310c93ca301dd96ef0f66c |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Use prompt: '**btdmnky**' to get a monkey. You can use the categories in the game to generate a monkey based on that category, such as putting "btdmnky magic" will generate a monkey based on the magic monkeys in-game. You can use: - primary - military - magic - support (results won't be great) - hero Some examples: ... | 34bc3430d31b7c142fe343c0d93e5781 |
apache-2.0 | [] | false | distilbert-base-ar-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy... | 92522925992dcfceeaf82d185a288916 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-ar-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-ar-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github r... | d5483a8237fd18ddb889fdf8f11d050b |
mit | ['audio', 'music', 'generation', 'tensorflow'] | false | Musika Misc Model Pretrained Misc GAN model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation. Introduced in [this paper](https://arxiv.org/abs/2208.08706). | caf9097dab0daa5f3a00b0918aa90a3d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Turkish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Turkish using [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | d771552f97c2d43601595459260a1f12 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | ", "!", "?", "«", "»", "(", ")", "؛", ",", "?", ".", "!", "-", ";", ":", '"', "“", "%", "‘", "�", "–", "…", "_", "”", '“', '„' ] chars_to_mapping = { "\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ", } def multiple_replace(text, chars_to_mapping): pattern = "|".join(map(re.escape, cha... | df87b9c17415b664b7dd99aa422b0452 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Turkish test data of Common Voice. ```python import librosa import torch import torchaudio from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets import load_dataset, load_metric import numpy as np import re import string chars_to_ignore = ... | d03f3040e14c01e51052241aa6dad877 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | ", "!", "?", "«", "»", "(", ")", "؛", ",", "?", ".", "!", "-", ";", ":", '"', "“", "%", "‘", "�", "–", "…", "_", "”", '“', '„' ] chars_to_mapping = { "\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ", "\u0307": " " } def multiple_replace(text, chars_to_mapping): pattern = "|".j... | 8ae8dee011c76313a03472c136e4d33e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training & Report The Common Voice `train`, `validation` datasets were used for training. You can see the training states [here](https://wandb.ai/m3hrdadfi/finetuned_wav2vec_xlsr_turkish/reports/Fine-Tuning-for-Wav2Vec2-Large-XLSR-53-Turkish--Vmlldzo1Njc1MDc?accessToken=02vm5cwbi7d342vyt7h9w9859zex0enltdmjoreyjt3bd5q... | d00292114eee516c16db02fdd092c54d |
mit | [] | false | Solo Levelling Art Style on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook | 860dd1d9d21f0e33177ea7b5a291fbc6 |
mit | [] | false | model by Classacre This your the Stable Diffusion model fine-tuned the Solo Levelling Art Style concept taught to Stable Diffusion with Dreambooth. You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen... | 499ea0b755a53f9614789fefcfba3157 |
mit | [] | false | 1028" This model is inspired by @ogkalu and his comic-diffusion model (https://huggingface.co/ogkalu/Comic-Diffusion). I think its pretty cool and you should check it out. I've made this model out of admiration towards Jang-Sung Rak (DUBU) who recently passed away. This model is not perfect, and will never be perfect ... | c86740d979a413764b8d59e5b56124cd |
mit | [] | false | - This new model uses the anythingv3.0 model as its base instead of the SD 1.5. This adds more dynamic backgrounds to the generations but strays abit away from the original style. - Characters and people are the same as V2 and have been improved to better reflect Jang-Sung Raks art style. - Action generations are oft... | 1e8fc99d3daf897d44c4606253e43a7e |
mit | [] | false | This is a massive improvement from the first version. I've split the model into two different models, one for non action generations (SoloLevellingCalm.ckpt) and one for action generations (SoloLevellingAction.ckpt). I plan on merging the two into one model in the future once I understand how to do captions. The calm ... | e9958268c679f60be919a6dc92b48593 |
mit | [] | false | It can be used by modifying the `instance_prompt(s)`: **sololeveling** This model was trained using 71 training images, 14200 total training steps, model saved every 3550 steps (25%) and text encoder was trained up to 35%. Made using Stable Diffusion v1.5 as the base model. The final model struggles to do calm / peac... | 93f576ddb56d938e8ed4a9539259ff4d |
creativeml-openrail-m | ['text-to-image'] | false | zombie_style Dreambooth model trained by sztanki 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/notebook... | 3502635dbbd371421878f9dc3d243897 |
apache-2.0 | ['generated_from_trainer'] | false | violation-classification-bantai-vit-v100ep This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.2557 - Accuracy: 0.9157 | d3589ca7ae030f521e315a956e67376a |
apache-2.0 | ['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: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | c20c7229f7e9da76f6b1cbfea3010063 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2811 | 1.0 | 101 | 0.2855 | 0.9027 | | 0.2382 | 2.0 | 202 | 0.2763 | 0.9085 | | 0.2361 | 3.0 | 303 | 0.2605 | 0.... | 737d53407161cecf503996cb9f990e8e |
apache-2.0 | ['catalan', 'textual entailment', 'teca', 'CaText', 'Catalan Textual Corpus'] | false | Model description The **roberta-base-ca-v2-cased-te** is a Textual Entailment (TE) model for the Catalan language fine-tuned from the [roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collec... | 290fb0e9edd3ffdc9ceeb13e160e8d71 |
apache-2.0 | ['catalan', 'textual entailment', 'teca', 'CaText', 'Catalan Textual Corpus'] | false | Intended uses and limitations **roberta-base-ca-v2-cased-te** model can be used to recognize Textual Entailment (TE). The model is limited by its training dataset and may not generalize well for all use cases. | 8b3506df33dc6e6b0e5a386829accbd3 |
apache-2.0 | ['catalan', 'textual entailment', 'teca', 'CaText', 'Catalan Textual Corpus'] | false | How to use Here is how to use this model: ```python from transformers import pipeline from pprint import pprint nlp = pipeline("text-classification", model="projecte-aina/roberta-base-ca-v2-cased-te") example = "M'agrada el sol i la calor. </s></s> A la Garrotxa plou molt." te_results = nlp(example) pprint(te_resu... | 61363d74d5361307a4d774ecfe6a9820 |
apache-2.0 | ['catalan', 'textual entailment', 'teca', 'CaText', 'Catalan Textual Corpus'] | false | Evaluation results We evaluated the roberta-base-ca-cased-te on the TE-ca test set against standard multilingual and monolingual baselines: | Model | TE-ca (Accuracy) | | ------------|:----| | roberta-base-ca-v2-cased-te | **83.14** | | BERTa | 79.26 | | mBERT | 74.63 | | XLM-RoBERTa | 33.30 | Fo... | 161a1195319009d88e9e54444c396754 |
apache-2.0 | ['catalan', 'textual entailment', 'teca', 'CaText', 'Catalan Textual Corpus'] | false | Citation information If you use any of these resources (datasets or models) in your work, please cite our latest paper: ```bibtex @inproceedings{armengol-estape-etal-2021-multilingual, title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}ata... | 26a3986041b0df80f9edd8505847bb87 |
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