Rewrite local model notes

Author: ruivieira

examples/Detoxify.ipynb

@@ -189,10 +189,12 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "## Initializing TMaRCo",
-   "id": "1eb7719e30054304"
+   "id": "1eb7719e30054304",
+   "metadata": {},
+   "source": [
+    "## Initializing TMaRCo"
+   ]
   },
   {
    "cell_type": "code",
@@ -205,43 +207,48 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "3e16ee305f4983d9",
+   "metadata": {},
    "source": [
     "This will initialize `TMaRCo` using the default models, taken from HuggingFace.\n",
     "<div class=\"alert alert-info\">\n",
-    "To use local models with TMaRCo, we need to have them in a local storage, accessible to TMaRCo, initialize separately, and pass them to TMaRCo's constructor.\n",
+    "To use local models with TMaRCo, we need to have the pre-initialized models in a local storage that is accessible to TMaRCo.\n",
     "</div>\n",
     "For instance, to use the default `facebook/bart-large` model, but locally. First, we would need to retrieve the model:"
-   ],
-   "id": "3e16ee305f4983d9"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "id": "614c9ff6f46a0ea9",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from huggingface_hub import snapshot_download\n",
     "\n",
     "snapshot_download(repo_id=\"facebook/bart-large\", local_dir=\"models/bart\")"
-   ],
-   "id": "614c9ff6f46a0ea9"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "We now initialize the base model and tokenizer from local files and pass them to `TMaRCo`:",
-   "id": "95bd792e757205d6"
+   "id": "95bd792e757205d6",
+   "metadata": {},
+   "source": [
+    "We now initialize the base model and tokenizer from local files and pass them to `TMaRCo`:"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
+   "execution_count": null,
+   "id": "f0f24485822a7c3f",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from transformers import BartForConditionalGeneration, BartTokenizer\n",
     "\n",
     "tokenizer = BartTokenizer.from_pretrained(\n",
-    "    \"models/bart\", # Or directory where the local model is stored \n",
+    "    \"models/bart\", # Or directory where the local model is stored\n",
     "    is_split_into_words=True, add_prefix_space=True\n",
     ")\n",
     "\n",
@@ -255,10 +262,7 @@
     "\n",
     "# Initialize TMaRCo with local models\n",
     "tmarco = TMaRCo(tokenizer=tokenizer, base_model=base)"
-   ],
-   "id": "f0f24485822a7c3f",
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "code",
@@ -286,29 +290,30 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "c113208c527c342e",
+   "metadata": {},
    "source": [
     "<div class=\"alert alert-info\">\n",
-    "To use local expert/anti-expert models with TMaRCo, we need to have them in a local storage, accessible to TMaRCo, as previously.\n",
+    "To use local expert/anti-expert models with TMaRCo, we need to have them in a local storage that is accessible to TMaRCo, as previously.\n",
+    "\n",
     "However, we don't need to initialize them separately, and can pass the directory directly.\n",
     "</div>\n",
     "If we want to use local models with `TMaRCo` (in this case the same default `gminus`/`gplus`):\n"
-   ],
-   "id": "c113208c527c342e"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "id": "dfa288dcb60102c",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "snapshot_download(repo_id=\"trustyai/gminus\", local_dir=\"models/gminus\")\n",
     "snapshot_download(repo_id=\"trustyai/gplus\", local_dir=\"models/gplus\")\n",
     "\n",
     "tmarco.load_models([\"models/gminus\", \"models/gplus\"])"
-   ],
-   "id": "dfa288dcb60102c"
+   ]
   },
   {
    "cell_type": "code",
@@ -450,21 +455,23 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "As noted previously, to use local models, simply pass the initialized tokenizer and base model to the constructor, and the local path as the expert/anti-expert:",
-   "id": "b0738c324227f57"
+   "id": "b0738c324227f57",
+   "metadata": {},
+   "source": [
+    "As noted previously, to use local models, simply pass the initialized tokenizer and base model to the constructor, and the local path as the expert/anti-expert:"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "id": "b929e21a97ea914e",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "tmarco = TMaRCo(tokenizer=tokenizer, base_model=base)\n",
     "tmarco.load_models([\"models/gminus\", \"models/gplus\"])"
-   ],
-   "id": "b929e21a97ea914e"
+   ]
   },
   {
    "cell_type": "markdown",