Yes, I did try this in the past. The thing is, unlike the link, I downloaded the model, stored it somewhere in my drive, and reloaded it using the google directory to model. So my code for reloading looks something like this:
I have a similar example but for a language modeling task. I stored it to google drive, reloaded in a colab notebook and it works for me. One difference that I noticed is that in the folder where I stored my fine tuned model I have additional files for you, like tokenizer.json and tokenizer_config.json.
I think that the tokenizer that you used when fine-tuned the model has not been exported in your directory and when the pipeline tries to load it, it fails. Did you try to load the same tokenizer used in fine tuning and pass it as an argument to the pipeline? (Pipelines)
If multiple classification labels are available (model.config.num_labels >= 2), the pipeline will run a softmax over the results. If there is a single label, the pipeline will run a sigmoid over the result.
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The extracellular matrix (ECM) has remained an enigmatic component of the tumor microenvironment. It drives metastasis via its interaction with the integrin signaling pathway, contributes to tumor progression and confers therapy resistance by providing a physical barrier around the tumor. The complexity of the ECM lies in its heterogeneous composition and complex glycosylation that can provide a support matrix as well as trigger oncogenic signaling pathways by interacting with the tumor cells. In this review, we attempt to dissect the role of the ECM in enriching for the treatment refractory cancer stem cell population and how it may be involved in regulating their metabolic needs. Additionally, we discuss how the ECM is instrumental in remodeling the tumor immune microenvironment and the potential ways to target this component in order to develop a viable therapy.
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