license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_keras_callback'] | false | BeardedJohn/bert-finetuned-seq-classification-fake-news 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: 0.0719 - Validation Loss: 0.0214 - Epoch: 0 | 92c96ac8864e6a1725d6ec1635fe826a |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 332, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': ... | ae6fe3c9e1208ea974a19076d9c10c17 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-SMALL-FF1000 (Deep-Narrow version) T5-Efficient-SMALL-FF1000 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* checkpo... | 57861d581a3236016d8de61e00159a89 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-small-ff1000** - is of model type **Small** with the following variations: - **ff** is **1000** It has **47.94** million parameters and thus requires *ca.* **191.75 MB** of memory in full precision (*fp32*) or **95.88 MB** of memory in half precisio... | bfcb40b5fbc9cfa79ca0efea95350e01 |
apache-2.0 | ['generated_from_trainer'] | false | Article_250v2_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article250v2_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2900 - Precision: 0.4665 - Recall: 0.5280 - F1: 0.4954 - Accuracy: ... | 9a4c74740ea20e6087003199c2534c80 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 29 | 0.4904 | 0.1788 | 0.0487 | 0.0765 | 0.8034 | | No log | 2.0 |... | 224d34191a21e07d3674f69ebb371761 |
mit | ['generated_from_trainer'] | false | poem-gen-spanish-t5-small-v7 This model is a fine-tuned version of [hackathon-pln-es/poem-gen-spanish-t5-small](https://huggingface.co/hackathon-pln-es/poem-gen-spanish-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9201 | 546ae2dc0e40083f654bb4f5824fba5f |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000333 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 | 2baf9855fd31eead3ef812e5ccac018e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 3.1716 | 0.73 | 30000 | 3.1114 | | 2.9666 | 1.46 | 60000 | 3.0271 | | 2.8292 | 2.19 | 90000 | 2.9531 | | 2.7264 | 2.93 | 120000 | 2... | aa498787952ffc0e2da606b4874f96b9 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | S2T-SMALL-MUSTC-EN-RU-ST `s2t-small-mustc-en-ru-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_... | 154feecb9ab4969c15d08b1343d31b79 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | Intended uses & limitations This model can be used for end-to-end English speech to Russian text translation. See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints. | 61aa02f576be548af7846cca0828ca87 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. *Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the filter bank features. Make sure... | 4df9be6a38e9968ddd4de357b1841df4 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | Training data The s2t-small-mustc-en-ru-st is trained on English-Russian subset of [MuST-C](https://ict.fbk.eu/must-c/). MuST-C is a multilingual speech translation corpus whose size and quality facilitates the training of end-to-end systems for speech translation from English into several languages. For each target ... | fd2e5209bcc001c86b954ff043104f6f |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | Preprocessing The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization) is applied to each example. The texts are lowercased and toke... | 87d928f17f57a6a463a4845366a5a474 |
apache-2.0 | ['exbert'] | false | BatteryBERT-uncased model
Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the [bert-base-cased](https://huggingface.co/bert-base-cased) weights. It was introduced in
[this paper](paper_link) and first released in
[this repository](https... | 853a1fb6462145ca718dd35acfa6046f |
apache-2.0 | ['exbert'] | false | Model description
BatteryBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the [bert-base-cased](https://huggingface.co/bert-base-cased) weights. This means
it was pretrained on the raw texts only, with no humans labelling them in any w... | 2a9763d2588bd72022dee1f56ec12153 |
apache-2.0 | ['exbert'] | false | Training data
The BatteryBERT model was pretrained on the full text of battery papers only, after initialized from the [bert-base-cased](https://huggingface.co/bert-base-cased) weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publisher... | 63fec8d79fb8a146fbe5cca8419fc546 |
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.
- I... | 5dac5c303a35c06ce107ecbdc0e1c64d |
apache-2.0 | ['exbert'] | false | Pretraining
The model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 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 2e-5, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
learning rate warmup for 10,... | 91833167e92b19d3fdc25e672393da7b |
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 primari... | c5083eda5f2a00a8f6843ccee16ad16e |
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/batterybert-cased')
>>> unmasker("Hello I'm a <mask> model.")
```
Here is how to use this model to get the featur... | e14bc53497a41f0f67828d22d1c40b88 |
apache-2.0 | ['automatic-speech-recognition', 'id'] | false | exp_w2v2t_id_xls-r_s411 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 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input i... | bd274d6f0b980dbb336f21ce21e43bc1 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0738 - Accuracy: 0.9756 | d852bb968660625a3c58edeb3b9ed6f8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2469 | 1.0 | 190 | 0.1173 | 0.9622 | | 0.1471 | 2.0 | 380 | 0.0806 | 0.9748 | | 0.1588 | 3.0 | 570 | 0.0738 | 0.... | 4102ec14c0b69bb335edc511bb8998ff |
cc-by-sa-4.0 | [] | false | sloberta-si-nli SloBERTa model finetuned on the SI-NLI dataset for Slovene natural language inference. Fine-tuned in a classic sequence pair classification setting on the official training/validation/test split for 10 epochs, using validation set accuracy for model selection. Optimized using the AdamW optimizer (le... | e4493be4ef7078650434c771f092c6a1 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the PAN-X dataset. The model is trained in Chapter 4: Multilingual Named Entity Recognition in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-lan... | 56a1d33abca7f4e0c7f16123a78a4990 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2912 | 1.0 | 835 | 0.1883 | 0.8238 | | 0.1548 | 2.0 | 1670 | 0.1738 | 0.8480 | | 0.101 | 3.0 | 2505 | 0.1739 | 0.8581 | ... | bbcd244a058264dae9c3b2e9b62d5603 |
gpl-2.0 | ['seals/CartPole-v0', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3', 'object-detection'] | false | How to use ```python from transformers import YolosFeatureExtractor, YolosForObjectDetection from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = YolosFeatureExtractor.from_pretrained('mhyatt00... | b8ece2e498cb5a2f6b90c51a1db1047e |
gpl-2.0 | ['seals/CartPole-v0', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3', 'object-detection'] | false | home) dataset. | Model | size (pixels) | mAPval | Speed | params | FLOPS | |---------------|-------------------|-----------|-----------|-----------|-----------| | YOLOv5s6 | 1280 | 43.3 | 4.3 | 12.7 | 17.4 | | YOLOv5m6 | 1280 | 50.5 | 8.4 | 35.9 | 52.4 | | YOLOv5l6 | 1... | 378b795d6f3340307b377721aa63a482 |
gpl-2.0 | ['seals/CartPole-v0', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3', 'object-detection'] | false | Bibtex and citation info ```bibtex @software{glenn_jocher_2022_6222936, author = {Glenn Jocher and Ayush Chaurasia and Alex Stoken and Jirka Borovec and NanoCode012 and Yonghye Kwon and TaoXie and ... | 6717080d3f97130500ed5b15bd1b7cfc |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_vp-sv_s179 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 53d05077b275ef0ed93f8d2d6cf934fa |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | openai/whisper-medium-nepali This model is a fine-tuned version of [shripadbhat/whisper-medium-hi](https://huggingface.co/shripadbhat/whisper-medium-hi) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 1.2050 - Wer: 26.8293 | 5676fbb44391257c5f4aa3dc42c81b12 |
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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | bb28d887bdfb063f67b1a0bb6264ecc5 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 10.0 | 10 | 1.2050 | 26.8293 | | 6ddcf0703a3f81bf31cc7a47647b095a |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-960h-finetuned-deepfake This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the asvspoof2019 dataset. It achieves the following results on the evaluation set: - Loss: 0.0009 - Accuracy: 0.9998 | bfb57d043e1574b3be60e414681b939a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 5983b266af5c3918d4e92315a4f6694e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0027 | 1.0 | 793 | 0.0070 | 0.9990 | | 0.0006 | 2.0 | 1586 | 0.0032 | 0.9995 | | 0.0271 | 3.0 | 2379 | 0.0022 | 0.... | 876c78629ac6c6f9887761b1e452b786 |
apache-2.0 | ['generated_from_keras_callback'] | false | cewinharhar/iceCream 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: - Train Loss: 1.1909 - Validation Loss: 3.0925 - Epoch: 92 | d9b1751b7090ccb3442fe911516e3573 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.9926 | 4.0419 | 0 | | 3.9831 | 3.8247 | 1 | | 3.8396 | 3.7337 | 2 | | 3.7352 | 3.6509 | 3 | | 3.6382 | 3.5948 | 4 | | 3.5595 |... | 52ef3959c0164b6f898818d8bad2beca |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | hubert-base-ft-keyword-spotting This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.0774 - Accuracy: 0.9819 | 310fdbba68538d31cf51592e050b368d |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0422 | 1.0 | 399 | 0.8999 | 0.6918 | | 0.3296 | 2.0 | 798 | 0.1505 | 0.9778 | | 0.2088 | 3.0 | 1197 | 0.0901 | 0.... | a2b61dffc99b4dfb8cb6bb444dad319f |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1378 - F1: 0.8616 | c9e7320d3ab454ee83d0117874ab5561 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2569 | 1.0 | 525 | 0.1617 | 0.8228 | | 0.1295 | 2.0 | 1050 | 0.1326 | 0.8514 | | 0.0816 | 3.0 | 1575 | 0.1378 | 0.8616 | ... | e6fca1f79b633a4be6d9a5043e1dcda5 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-BERTino This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It was trained on a dataset made from question/context pairs ([squad-it](https://github.com/crux82/squad... | 5798eeca0811404bb6bc6cced9906b18 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | 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 = ["Questo è un esempio di... | eebf4b4f4245011e558c258889b6121c |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-sec This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0856 - Rouge1: 32.2284 - Rouge2: 28.534 - Rougel: 31.5055 - Rougelsum: 31.5557 - Gen Len: 19.0 | 37fd9a1df20feee01c25a9da64fa6ed4 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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: 1000 - mixed_precision_training: Native AMP | 14ff62b231130db90ddfdeadaeac1817 |
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 | 7 | 4.6983 | 11.362 | 2.7982 | 8.7377 | 9.7976 |... | 3beb0785f51f5009f0b8d1945984c3fb |
mit | ['exbert'] | false | KRISSBERT [https://arxiv.org/pdf/2112.07887.pdf](https://arxiv.org/pdf/2112.07887.pdf) Entity linking faces significant challenges such as prolific variations and prevalent ambiguities, especially in high-value domains with myriad entities. Standard classification approaches suffer from the annotation bottleneck and... | ff763b63bd567daeaf8051bc8fa23cc4 |
mit | ['exbert'] | false | Usage for Entity Linking Here, we use the [MedMentions](https://github.com/chanzuckerberg/MedMentions) data to show you how to 1) **generate prototype embeddings**, and 2) **run entity linking**. (We are currently unable to release the self-supervised mention examples, because they require the UMLS and PubMed licens... | 53d0f4053bd2942dbc4814805364cb54 |
mit | ['exbert'] | false | Citation If you find KRISSBERT useful in your research, please cite the following paper: ```latex @article{krissbert, author = {Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon}, title = {Knowledge-Rich Self-Supervision for Biomedical ... | 299242da6b3c82119e0157a347938de9 |
apache-2.0 | ['pythae', 'reproducibility'] | false | This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from pythae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_vae") ``` | 333a6ec2c60b82171920ab28ec5ae545 |
apache-2.0 | ['pythae', 'reproducibility'] | false | Reproducibility This trained model reproduces the results of the VAE used in Table 1 in [1]. | Model | Dataset | Metric | Obtained value | Reference value | |:---:|:---:|:---:|:---:|:---:| | VAE | Binary MNIST | NLL (200 IS) | 89.78 (0.01) | 89.9 | [1] Danilo Rezende and Shakir Mohamed. Variational inference with no... | eecd3bcf9694af319614a809ef88b09d |
apache-2.0 | ['generated_from_trainer'] | false | koelectra-base-v3-discriminator-finetuned-klue-v4 This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6219 | f5567a0d51a43c68c86f98cdb1fa44fe |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.4979 | 0.33 | 500 | 4.0470 | | 3.2001 | 0.65 | 1000 | 2.3172 | | 2.215 | 0.98 | 1500 | 1.9043 | | 1.7849 | 1.31 | 2000 | 1.7181 ... | 1572059b5f28c321595c8827552e3a92 |
apache-2.0 | ['generated_from_trainer'] | false | resnet-50-finetuned-eurosat This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1981 - Accuracy: 0.6677 | 4811921e1daa6ee25a979961d71da69a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5279 | 0.99 | 70 | 1.5218 | 0.6677 | | 1.1982 | 1.99 | 140 | 1.2405 | 0.6677 | | 1.0836 | 2.99 | 210 | 1.1981 | 0.... | 38d3355a215d971080cf576fb293d561 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_stsb 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: 1.4410 - Pearson: 0.1664 - Spearmanr: 0.1770 - Combined Sc... | 46dde322c6ad2a891fe415981a0f53d5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:---------:|:--------------:| | 0.5057 | 1.0 | 2518 | 1.4410 | 0.1664 | 0.1770 | 0.1717 | | 0.2904 | 2.0 | 50... | 0e5d01d5111a26e0004b9e8ae271517a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Frisian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Frisian using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. | 7745a4b00b805ee308733e50876a17ac |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "fy-NL", split="test[:2%]"). processor = Wav2Vec2Processor.fr... | c9deb9b748712b9cefd9cbddc1ec472a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Frisian test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "fy-NL", split="test") ... | 9c650912dac87c43303ebed11f072884 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice `train`, `validation` datasets were used for training. The script used for training can be found [here](https://github.com/AMontgomerie/wav2vec2-xlsr/blob/main/Frisian/XLSR_Frisian.ipynb) A notebook of the evaluation script can be found [here](https://github.com/AMontgomerie/wav2vec2-xlsr/... | e006aa1ccf3246b64689af0d3f7082d4 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-wnli This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6959 - Accuracy: 0.5634 | 3209072dee5661c2b0f4777e416dee8c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 20 | 0.6933 | 0.5493 | | No log | 2.0 | 40 | 0.6959 | 0.5634 | | No log | 3.0 | 60 | 0.6978 | 0.... | 1b84b2dd7b1f0c5254e678fb8b29b354 |
apache-2.0 | ['translation'] | false | opus-mt-de-bzs * source languages: de * target languages: bzs * OPUS readme: [de-bzs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-bzs/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | 103eee607309dc30d60d7576988a1027 |
mit | [] | false | chop on Stable Diffusion This is the `<Le Petit Prince>` 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 ... | c2e8d4099286ce70704e9f6788e79756 |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-patch16-224-in21k-shiba-inu-detector This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on dataset with 4 dog types including Shiba Inu. It achieves the following results on the evaluation set: - Loss: 0.6511 - Accuracy: 1.0 | 142c8be50b9074c2179287f40c5a5aaa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.94 | 4 | 1.3875 | 0.1667 | | No log | 1.94 | 8 | 1.2712 | 0.7833 | | 1.4176 | 2.94 | 12 | 1.0972 | 0.... | a87094ecca93536e10edd1c737ef30e4 |
apache-2.0 | ['job advertisement', 'turkish bert', 'bert-based', 'StratifiedKFold'] | false | About the model It has been trained with 15451 real job advertisement data. Included classes; - Uygun İlan - Is Ilani Degil - Mustehcen - Cift Pozisyon Accordingly, the success rates in education are as follows; - **Model is Turkish bert-based.** - **Used StratifiedKFold(5) for validation.** - results [0.80685862... | da08f128021314baa015e5724e5b3d08 |
apache-2.0 | ['job advertisement', 'turkish bert', 'bert-based', 'StratifiedKFold'] | false | Example **!IMPORTANT_HINT: The sentence given to pipe must not contain Turkish characters.** ```sh from transformers import AutoTokenizer, TextClassificationPipeline, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nanelimon/bert-base-turkish-job-advertisement") model = AutoModelForSeq... | ac878bd6e146e4438986ef309bde9eef |
creativeml-openrail-m | ['text-to-image'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [... | 3fe4f561ce700f37df99d04ea5d9aba4 |
apache-2.0 | ['generated_from_trainer'] | false | STT_Model_8 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5858 - Wer: 0.3549 | d26cc6b0f28171356f5f40effb239a5e |
apache-2.0 | ['generated_from_trainer'] | false | Dataset info - Name: LJSpeech - Source: https://www.kaggle.com/datasets/mathurinache/the-lj-speech-dataset - Total audios (in Google Drive): 1420 - Total transcripts (in Google Drive): 13100 - No. of rows selected: 100 - Train-test ratio: 80:20 - No. of training set: 80 - No. of testing set: 20 | 1726a3351bfd8ac2f1111e93a8249378 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 20.0 | 200 | 2.9443 | 1.0 | | No log | 40.0 | 400 | 2.8603 | 1.0 | | 3.8362 | 60.0 | 600 | 0.5940 | 0.4197 | |... | 38bc7cb53605189e94504e4cc1aa6616 |
mit | ['generated_from_trainer'] | false | result This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7458 | bb1c3693ba4af5db34e2522972099054 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | ef66e8d68321338b256e83ee4ab25930 |
openrail | [] | false | How to setup the model ======================  Thank you for downloading and trying the Tokkyu Model. You need to do a quick setup to get the best results. Here is a quick visual guide with minimal text. The most important part of the process is the Aesthetic Gradients co... | a50a3f464e5bbe24c3d53dfec0ccd234 |
mit | ['generated_from_trainer'] | false | poem-gen-spanish-t5-small-v6 This model is a fine-tuned version of [hackathon-pln-es/poem-gen-spanish-t5-small](https://huggingface.co/hackathon-pln-es/poem-gen-spanish-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8831 | c0c25069533daa548a4142240cdad537 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | e05353bf40051c4f7b7e9de0192d13c0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 2.8551 | 0.73 | 30000 | 2.9296 | | 2.6961 | 1.46 | 60000 | 2.9005 | | 2.5756 | 2.19 | 90000 | 2.8786 | | 2.5095 | 2.93 | 120000 | 2... | c8fbf288ca25c4179d69574dbd3ef042 |
apache-2.0 | [] | false | Basic info model based [Salesforce/codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono) fine-tuned with data [codeparrot/github-code-clean](https://huggingface.co/datasets/codeparrot/github-code-clean) data filter by JavaScript and TypeScript | 34ef066595081c8b19056114660b822e |
apache-2.0 | [] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_type = 'kdf/javascript-docstring-generation' tokenizer = AutoTokenizer.from_pretrained(model_type) model = AutoModelForCausalLM.from_pretrained(model_type) inputs = tokenizer('''<|endoftext|> function getDateAfterNDay(n){ return ... | 8db39caa446e2af78714006409b06577 |
apache-2.0 | [] | false | Prompt You could give model a style or a specific language, for example: ```python inputs = tokenizer('''<|endoftext|> function add(a, b){ return a + b; } // docstring /** * Calculate number add. * @param a {number} the first number to add * @param b {number} the second number to add * @return the result... | c0918fb5ee1143bbeca3b5a09f5ad546 |
apache-2.0 | [] | false | <|endoftext|> ) ret = tokenizer.decode(generated_ids.sequences[0], skip_special_tokens=False) print(ret) inputs = tokenizer('''<|endoftext|> function add(a, b){ return a + b; } // docstring /** * 计算数字相加 * @param a {number} 第一个加数 * @param b {number} 第二个加数 * @return 返回 a + b 的结果 */ <|endoftext|> function... | 7bd6a48013dc7239b175e1caa790f820 |
apache-2.0 | ['bart', 'biobart', 'biomedical'] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 特殊 Special | 领域 Domain | 余元 Yuyuan | BioBART | 400M | 英文 English | | 8b74129418d86682a5a7913f5afd1737 |
apache-2.0 | ['bart', 'biobart', 'biomedical'] | false | 模型信息 Model Information Paper: [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model](https://arxiv.org/pdf/2204.03905.pdf) Yuyuan-Bart-139M由清华大学和IDEA研究院一起提供的生物医疗领域的生成语言模型。我们使用PubMed上的生物医学研究论文摘要（约41G）作为预训练语料。使用开源框架DeepSpeed的情况下，我们在2个带有16个40GB A100 GPU的DGX结点上对BioBART-large（400M参数）进行了约168小时的训练。... | fd54c4a71fdbe174051306946e913c5c |
apache-2.0 | ['bart', 'biobart', 'biomedical'] | false | 使用 Usage ```python from transformers import BartForConditionalGeneration, BartTokenizer tokenizer = BartTokenizer.from_pretrained('IDEA-CCNL/Yuyuan-Bart-400M') model = BartForConditionalGeneration.from_pretrained('IDEA-CCNL/Yuyuan-Bart-400M') text = 'Influenza is a <mask> disease.' input_ids = tokenizer([text], retu... | 0219676873b6b5345a516b23a456477a |
apache-2.0 | ['bart', 'biobart', 'biomedical'] | false | 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的对该模型的论文： If you are using the resource for your work, please cite the our paper for this model: ``` @misc{BioBART, title={BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model}, author={Hongyi Yuan and Zheng Yuan and Ruyi Gan and Jiaxing Zhang and Yut... | 464c93f805d178bb7f1efa9bcfeda7e4 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'cv', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-chuvash This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - CV dataset. It achieves the following results on the evaluation set: - Loss: 0.7651 - Wer: 0.6166 | 4fc92d6bf3a06125d40c6b0806a99474 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'cv', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.8032 | 8.77 | 500 | 0.8059 | 0.8352 | | 1.2608 | 17.54 | 1000 | 0.5828 | 0.6769 | | 1.1337 | 26.32 | 1500 | 0.6892 | 0.6908 | |... | 46762812fe097411b98e79997ca25472 |
mit | ['torch'] | false | BERT BASE (cased) Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is cased: it does make a difference between bulgar... | ae9a9432f34be93d3e7952b21e0cd44e |
mit | ['torch'] | false | Model description The model was trained similarly to [RuBert](https://arxiv.org/pdf/1905.07213.pdf) wherein the Multilingual Bert was adapted for the Russian language. The training data was Bulgarian text from [OSCAR](https://oscar-corpus.com/post/oscar-2019/), [Chitanka](https://chitanka.info/) and [Wikipedia](http... | 859de2d76685d2a919e136ea7d1318e9 |
mit | ['torch'] | false | How to use Here is how to use this model in PyTorch: ```python >>> from transformers import pipeline >>> >>> model = pipeline( >>> 'fill-mask', >>> model='rmihaylov/bert-base-bg', >>> tokenizer='rmihaylov/bert-base-bg', >>> device=0, >>> revision=None) >>> output = model("София е [MASK] на Бълга... | d71910ac7f3f6c40ba9992a6184efc59 |
apache-2.0 | ['generated_from_trainer'] | false | mt5-small-finetuned-4epochs-opus_books-en-to-it This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the opus_books dataset. It achieves the following results on the evaluation set: - Loss: 2.7675 | b559b00470843d0a3fd6deb7c3d6ee11 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.5854 | 1.0 | 3638 | 2.8808 | | 3.4992 | 2.0 | 7276 | 2.8156 | | 3.4374 | 3.0 | 10914 | 2.7796 | | 3.3984 | 4.0 | 14552 | 2.7675 ... | 7d57f421ffa302f6285c344d83d00800 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0611 - Precision: 0.9230 - Recall: 0.9366 - F1: 0.9298 - Accuracy: 0.9832 | 11c1dbe4c1e36b427f10cadadf1d4072 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2349 | 1.0 | 878 | 0.0736 | 0.9140 | 0.9211 | 0.9175 | 0.9803 | | 0.0546 | 2.0 |... | e8c4e4f3df7cf751e5687d0e10bb627c |
cc-by-4.0 | ['question-answering, multi-step-reasoning, multi-hop-reasoning'] | false | digit_tokenization.py from https://github.com/stonybrooknlp/teabreac model_name = "StonyBrookNLP/teabreac-preasm-large-iirc-retrieved" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) | b4d33b66aef1d9d32b4edc43d112883c |
apache-2.0 | ['generated_from_keras_callback'] | false | levgil2/stam-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8517 - Validation Loss: 2.5705 - Epoch: 0 | 10ceb3f72f40d15c28255b1c48d98739 |
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