Fix typo in evaluation script name

Readme.md

@@ -24,7 +24,7 @@ I only tested this code with Ubuntu 12.04, but I tried to make it as generic as
 2. **Get the data.** I don't distribute the data in the Git repo, instead download the `data/` folder from [here](http://cs.stanford.edu/people/karpathy/deepimagesent/). Also, this download does not include the raw image files, so if you want to visualize the annotations on raw images, you have to obtain the images from Flickr8K / Flickr30K / COCO directly and dump them into the appropriate data folder.
 3. **Train the model.** Run the training `$ python driver.py` (see many additional argument settings inside the file) and wait. You'll see that the learning code writes checkpoints into `cv/` and periodically reports its status in `status/` folder. 
 4. **Monitor the training.** The status can be inspected manually by reading the JSON and printing whatever you wish in a second process. In practice I run cross-validations on a cluster, so my `cv/` folder fills up with a lot of checkpoints that I further filter and inspect with other scripts. I am including my cluster training status visualization utility as well if you like. Run a local webserver (e.g. `$ python -m SimpleHTTPServer 8123`) and then open `monitorcv.html` in your browser on `http://localhost:8123/monitorcv.html`, or whatever the web server tells you the path is. You will have to edit the file to setup the paths properly and point it at the right json files.
-5. **Evaluate model checkpoints.** To evaluate a checkpoint from `cv/`, run the `evaluate_sentence_predctions.py` script and pass it the path to a checkpoint.
+5. **Evaluate model checkpoints.** To evaluate a checkpoint from `cv/`, run the `eval_sentence_predictions.py` script and pass it the path to a checkpoint.
 6. **Visualize the predictions.** Use the included html file `visualize_result_struct.html` to visualize the JSON struct produced by the evaluation code. This will visualize the images and their predictions. Note that you'll have to download the raw images from the individual dataset pages and place them into the corresponding `data/` folder.
 
 Lastly, note that this is currently research code, so a lot of the documentation is inside individual Python files. If you wish to work with this code, you'll have to get familiar with it and be comfortable reading Python code.