[0,2,1,0]) 4 metrics File ~/anaconda3/envs/NER/lib/python3.10/site-packages/evaluate/module.py:862, in CombinedEvaluations.compute(self, predictions, references, **kwargs) 860 batch = {"predictions": predictions, "references": references, **kwargs} 861 batch = {input_name: batch[...
args=TrainingArguments(# output_dir:directory where the model checkpoints will be saved.output_dir=model_output_dir,#evaluation_strategy(default"no"):# Possible values are:#"no":No evaluation is done during training.#"steps":Evaluation isdone(and logged)every eval_steps.#"epoch":Evaluation is ...
# away the pretraining head of the BERT model to replace it with a classification # head which is randomly initialized. We will fine-tune this model on our task, # transferring the knowledge of the pretrained model to it (which is why doing # this is called transfer learning). 在编写训...
# away the pretraining head of the BERT model to replace it with a classification # head which is randomly initialized. We will fine-tune this model on our task, # transferring the knowledge of the pretrained model to it (which is why doing # this is called transfer learning). 在编写训...
scores = model.predict([("The weather today is beautiful","It's raining!"), ("The weather today is beautiful","Today is a sunny day")]) scores array([0.46552283, 0.6350213 ], dtype=float32) 检索和重排序 现在我们已经了解了交叉编码器和双向编码器的不同,让我们看看如何使在实践中用它们来...
# eval_steps: Number of update steps between two evaluations if # evaluation_strategy="steps". Will default to the same value as # logging_steps if not set. eval_steps=50, # logging_strategy (default: "steps"): The logging strategy to adopt during ...
# the model. return tokenizer(examples["text"], truncation=True) # batched=True: use this if you have a mapped function which can efficiently # handle batches of inputs like the tokenizer splitted_datasets_encoded = splitted_datasets.map(preprocess_function_batch, batched=True) ...
While evolving Lighteval into its own standalone tool, we are grateful to the Harness and HELM teams for their pioneering work on LLM evaluations. 🌟 Contributions Welcome 💙💚💛💜🧡 Got ideas? Found a bug? Want to add ataskormetric? Contributions are warmly welcomed!
cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None ) return model TrainingArgument args:超参数的定义,这部分也是trainer的重要功能,大部分训练相关的参数都是这里设置的,非常的方便: ...
本期的 31 篇论文如下: [00:23] 📊 MixEval-X: Any-to-Any Evaluations from Real-World Data Mixtures(MixEval-X:从现实世界数据混合中进行任意到任意评估) [01:02] 🎥 Movie Gen: A Cast of Media Foundation Models(电影生成:媒体基础模型集合) [01:35] 📱 MobA:...